An expression is the syntactic unit in a Stan program that denotes a value. Every expression in a well-formed Stan program has a type that is determined statically (at compile time), based only on the type of its variables and the types of the functions used in it. If an expressions type cannot be determined statically, the Stan compiler will report the location of the problem.

This chapter covers the syntax, typing, and usage of the various forms of expressions in Stan.

Numeric literals

The simplest form of expression is a literal that denotes a primitive numerical value.

Integer literals

Integer literals represent integers of type int. Integer literals are written in base 10 without any separators. Integer literals may contain a single negative sign. (The expression --1 is interpreted as the negation of the literal -1.)

The following list contains well-formed integer literals.

0, 1, -1, 256, -127098, 24567898765

Integer literals must have values that fall within the bounds for integer values (see the section on numerical data types).

Integer literals may not contain decimal points (.). Thus the expressions 1. and 1.0 are of type real and may not be used where a value of type int is required.

Real literals

A number written with a period or with scientific notation is assigned to a the continuous numeric type real. Real literals are written in base 10 with a period (.) as a separator and optionally an exponent with optional sign. Examples of well-formed real literals include the following.

0.0, 1.0, 3.14, -217.9387, 2.7e3, -2E-5, 1.23e+3.

The notation e or E followed by a positive or negative integer denotes a power of 10 to multiply. For instance, 2.7e3 and 2.7e+3 denote \(2.7 \times 10^3\), whereas -2E-5 denotes \(-2 \times 10^{-5}\).

Imaginary literals

A number followed by the character i denotes an imaginary number and is assigned to the numeric type complex. The number preceding i may be either a real or integer literal and determines the magnitude of the imaginary number. Examples of well-formed imaginary literals include the following.

1i, 2i, -325.786i, 1e10i, 2.87e-10i.

Note that the character i by itself is not a well-formed imaginary literal. The unit imaginary number must be written as 1i.

Complex literals

Stan does not include complex literals directly, but a real or integer literal can be added to an imaginary literal to derive an expression that behaves like a complex literal. Examples include the following.

1 + 2i, -3.2e9 + 1e10i

These will be assigned the type complex, which is the result of adding a real or integer and a complex number. They will also function like literals in the sense that the C++ compiler is able to reduce them to a single complex constant at compile time.


A variable by itself is a well-formed expression of the same type as the variable. Variables in Stan consist of ASCII strings containing only the basic lower-case and upper-case Roman letters, digits, and the underscore (_) character. Variables must start with a letter (a--z and A--Z) and may not end with two underscores (__).

Examples of legal variable identifiers are as follows.

a, a3, a_3, Sigma, my_cpp_style_variable, myCamelCaseVariable

Unlike in R and BUGS, variable identifiers in Stan may not contain a period character.

Reserved names

Stan reserves many strings for internal use and these may not be used as the name of a variable. An attempt to name a variable after an internal string results in the stanc translator halting with an error message indicating which reserved name was used and its location in the model code.

Model name

The name of the model cannot be used as a variable within the model. This is usually not a problem because the default in bin/stanc is to append _model to the name of the file containing the model specification. For example, if the model is in file foo.stan, it would not be legal to have a variable named foo_model when using the default model name through bin/stanc. With user-specified model names, variables cannot match the model.

Reserved words from Stan language

The following list contains reserved words for Stan’s programming language. Not all of these features are implemented in Stan yet, but the tokens are reserved for future use.

for, in, while, repeat, until, if, then, else,
true, false, target, struct, typedef, export,
auto, extern, var, static, lower, upper, offset,

Variables should not be named after types, either, and thus may not be any of the following.

int, real, complex, vector, simplex, unit_vector,
ordered, positive_ordered, row_vector, matrix,
cholesky_factor_corr, cholesky_factor_cov,
corr_matrix, cov_matrix, array

The following built in functions are also reserved and cannot be used as variable names:

print, reject, profile, fatal_error, target

The following block identifiers are reserved and cannot be used as variable names:

functions, model, data, parameters, quantities,
transformed, generated

Reserved distribution names

Variable names will also conflict with the names of distributions suffixed with _lpdf, _lpmf, _lcdf, and _lccdf, _cdf, and _ccdf, such as normal_lcdf_log. No user-defined variable can take a name ending in _lupdf or _lupmf even if a corresponding _lpdf or _lpmf is not defined.

Using any of these variable names causes the stanc translator to halt and report the name and location of the variable causing the conflict.

Reserved names backend languages

Stan primarily generates code in C++, which features its own reserved words. It is legal to name a variable any of the following names, however doing so will lead to it being renamed _stan_NAME (e.g. _stan_public) behind the scenes (in the generated C++ code).

alignas, alignof, and, and_eq, asm, bitand, bitor, bool,
case, catch, char, char16_t, char32_t, class, compl, const,
constexpr, const_cast, decltype, default, delete, do,
double, dynamic_cast, enum, explicit, float, friend, goto,
inline, long, mutable, namespace, new, noexcept, not, not_eq,
nullptr, operator, or, or_eq, private, protected, public,
register, reinterpret_cast, short, signed, sizeof,
static_assert, static_cast, switch, template, this, thread_local,
throw, try, typeid, typename, union, unsigned, using, virtual,
volatile, wchar_t, xor, xor_eq, fvar, STAN_MAJOR, STAN_MINOR,

Container expressions

Expressions for the Stan container objects, namely arrays, vectors, row vectors, matrices, and tuples, can all be constructed using expressions.

Vector expressions

Square brackets may be wrapped around a sequence of comma separated primitive expressions to produce a row vector expression. For example, the expression [ 1, 10, 100 ] denotes a row vector of three elements with real values 1.0, 10.0, and 100.0. Applying the transpose operator to a row vector expression produces a vector expression. This syntax provides a way declare and define small vectors a single line, as follows.

row_vector[2] rv2 =  [ 1, 2 ];
vector[3] v3 = [ 3, 4, 5 ]';

The vector expression values may be compound expressions or variable names, so it is legal to write [ 2 * 3, 1 + 4] or [ x, y ], providing that x and y are primitive variables.

Matrix expressions

A matrix expression consists of square brackets wrapped around a sequence of comma separated row vector expressions. This syntax provides a way declare and define a matrix in a single line, as follows.

matrix[3, 2] m1 = [ [ 1, 2 ], [ 3, 4 ], [5, 6 ] ];

Any expression denoting a row vector can be used in a matrix expression. For example, the following code is valid:

vector[2] vX = [ 1, 10 ]';
row_vector[2] vY = [ 100, 1000 ];
matrix[3, 2] m2 = [ vX', vY, [ 1, 2 ]  ];

Complex vector and matrix expressions

Complex vector expressions work the same way as real vector expressions. For example, the following are all legal Stan expressions and assignments.

complex_vector[3] = [1 + 2i, 3 - 1.7i, 0]';
complex_row_vector[2] = [12, -2i];
complex_matrix[2, 3] = [[1 + 2i, 3 - 1.7i, 0],
                        [3.9 - 1.234i, 176i, 1 + 1i]];

No empty vector or matrix expressions

The empty expression [ ] is ambiguous and therefore is not allowed and similarly expressions such as [ [ ] ] or [ [ ], [ ] ] are not allowed.

Array expressions

Curly braces may be wrapped around a sequence of expressions to produce an array expression. For example, the expression { 1, 10, 100 } denotes an integer array of three elements with values 1, 10, and 100. This syntax is particularly convenient to define small arrays in a single line, as follows.

array[3] int a = { 1, 10, 100 };

The values may be compound expressions, so it is legal to write { 2 * 3, 1 + 4 }. It is also possible to write two dimensional arrays directly, as in the following example.

array[2, 3] int b = { { 1, 2, 3 }, { 4, 5, 6 } };

This way, b[1] is { 1, 2, 3 } and b[2] is { 4, 5, 6 }.

Whitespace is always interchangeable in Stan, so the above can be laid out as follows to more clearly indicate the row and column structure of the resulting two dimensional array.

array[2, 3] int b = { { 1, 2, 3 },
                { 4, 5, 6 } };

Array expression types

Any type of expression may be used within braces to form an array expression. In the simplest case, all of the elements will be of the same type and the result will be an array of elements of that type. For example, the elements of the array can be vectors, in which case the result is an array of vectors.

vector[3] b;
vector[3] c;
// ...
array[2] vector[3] d = { b, c };

The elements may also be a mixture of int and real typed expressions, in which case the result is an array of real values.

array[2] real b = { 1, 1.9 };

Tuple expressions and types

Stan uses parentheses around a comma-separated sequence of expressions to construct a tuple. For example, we can construct a 2-tuple as follows.

tuple(int, vector[3]) xy = (42, [1, 2.9, -1.3]');

The expression 42 is of type int and the expression [1, 2.9, -1.3] is of type row_vector so that [1, 2.9, -1.3]' is of type vector and of size 3. The whole tuple expression (42, [1, 2.9, -1.3]') thus has a sized type of tuple(int, vector[3]) and an unsized type (e.g., for a function argument) of tuple(int, vector).

Stan does not support the Python notation with trailing commas, such as (1, 2, 3, ) for a 3-tuple.

Restrictions on values

There are some restrictions on how array expressions may be used that arise from their types being calculated bottom up and the basic data type and assignment rules of Stan.

Rectangular array expressions only

Although it is tempting to try to define a ragged array expression, all Stan data types are rectangular (or boxes or other higher-dimensional generalizations). Thus the following nested array expression will cause an error when it tries to create a non-rectangular array.

{ { 1, 2, 3 }, { 4, 5 } }  // compile time error: size mismatch

This may appear to be OK, because it is creating a two-dimensional integer array (array[,] int) out of two one-dimensional array integer arrays (array[] int). But it is not allowed because the two one-dimensional arrays are not the same size. If the elements are array expressions, this can be diagnosed at compile time. If one or both expressions is a variable, then that won’t be caught until runtime.

{ { 1, 2, 3 }, m }  // runtime error if m not size 3

No empty array expressions

Because there is no way to infer the type of the result, the empty array expression ({ }) is not allowed. This does not sacrifice expressive power, because a declaration is sufficient to initialize a zero-element array.

array[0] int a;   // a is fully defined as zero element array

No zero-tuples or one-tuples

There is no way to declare or construct a zero-tuple or one-tuple in Stan. Tuples must be at least two elements long. The expression () does not pick out a zero-tuple—it is ill formed. Similarly, the expression (1) is of type int rather than a tuple.

Parentheses for grouping

Any expression wrapped in parentheses is also an expression. Like in C++, but unlike in R, only the round parentheses, ( and ), are allowed. The square brackets [ and ] are reserved for array indexing and the curly braces { and } for grouping statements.

With parentheses it is possible to explicitly group subexpressions with operators. Without parentheses, the expression 1 + 2 * 3 has a subexpression 2 * 3 and evaluates to 7. With parentheses, this grouping may be made explicit with the expression 1 + (2 * 3). More importantly, the expression (1 + 2) * 3 has 1 + 2 as a subexpression and evaluates to 9.

Arithmetic and matrix operations on expressions

For integer and real-valued expressions, Stan supports the basic binary arithmetic operations of addition (+), subtraction (-), multiplication (*) and division (/) in the usual ways.

For integer expressions, Stan supports the modulus (%) binary arithmetic operation. Stan also supports the unary operation of negation for integer and real-valued expressions. For example, assuming n and m are integer variables and x and y real variables, the following expressions are legal.

3.0 + 0.14
2 * 3 + 1
(x - y) / 2.0
(n * (n + 1)) / 2
x / n
m % n

The negation, addition, subtraction, and multiplication operations are extended to matrices, vectors, and row vectors. The transpose operation, written using an apostrophe (') is also supported for vectors, row vectors, and matrices. Return types for matrix operations are the smallest types that can be statically guaranteed to contain the result. The full set of allowable input types and corresponding return types is detailed in the list of functions.

For example, if y and mu are variables of type vector and Sigma is a variable of type matrix, then (y - mu)' * Sigma * (y - mu) is a well-formed expression of type real. The type of the complete expression is inferred working outward from the subexpressions. The subexpression(s) y - mu are of type vector because the variables y and mu are of type vector. The transpose of this expression, the subexpression (y - mu)' is of type row_vector. Multiplication is left associative and transpose has higher precedence than multiplication, so the above expression is equivalent to the following fully specified form (((y - mu)') * Sigma) * (y - mu).

The type of subexpression (y - mu)' * Sigma is inferred to be row_vector, being the result of multiplying a row vector by a matrix. The whole expression’s type is thus the type of a row vector multiplied by a (column) vector, which produces a real value.

Stan provides elementwise matrix multiplication (e.g., a .* b) and division (e.g., a ./ b) operations. These provide a shorthand to replace loops, but are not intrinsically more efficient than a version programmed with an elementwise calculations and assignments in a loop. For example, given declarations,

vector[N] a;
vector[N] b;
vector[N] c;

the assignment,

c = a .* b;

produces the same result with roughly the same efficiency as the loop

for (n in 1:N) {
  c[n] = a[n] * b[n];

Stan supports exponentiation (^) of integer and real-valued expressions. The return type of exponentiation is always a real-value. For example, assuming n and m are integer variables and x and y real variables, the following expressions are legal.

3 ^ 2
3.0 ^ -2
3.0 ^ 0.14
x ^ n
n ^ x
n ^ m
x ^ y

Exponentiation is right associative, so the expression 2 ^ 3 ^ 4 is equivalent to the fully specified form 2 ^ (3 ^ 4).

Operator precedence and associativity

The precedence and associativity of operators, as well as built-in syntax such as array indexing and function application is given in tabular form in the following table.

Operator Precedence Table

Stan’s unary, binary, and ternary operators, with their precedences, associativities, place in an expression, and a description. The last two lines list the precedence of function application and array, matrix, and vector indexing. The operators are listed in order of precedence, from least tightly binding to most tightly binding. The full set of legal arguments and corresponding result types are provided in the function documentation for the operators (i.e., operator*(int, int):int indicates the application of the multiplication operator to two integers, which returns an integer). Parentheses may be used to group expressions explicitly rather than relying on precedence and associativity.

Op. Prec. Assoc. Placement Description
? ~ : 10 right ternary infix conditional
|| 9 left binary infix logical or
&& 8 left binary infix logical and
== 7 left binary infix equality
!= 7 left binary infix inequality
< 6 left binary infix less than
<= 6 left binary infix less than or equal
> 6 left binary infix greater than
>= 6 left binary infix greater than or equal
+ 5 left binary infix addition
- 5 left binary infix subtraction
* 4 left binary infix multiplication
.* 4 left binary infix elementwise multiplication
/ 4 left binary infix (right) division
./ 4 left binary infix elementwise division
% 4 left binary infix modulus
\ 3 left binary infix left division
%/% 3 left binary infix integer division
! 2 n/a unary prefix logical negation
- 2 n/a unary prefix negation
+ 2 n/a unary prefix promotion (no-op in Stan)
^ 1 right binary infix exponentiation
.^ 1 right binary infix elementwise exponentiation
' 0 n/a unary postfix transposition
() 0 n/a prefix, wrap function application
[] 0 left prefix, wrap array, matrix indexing

Other expression-forming operations, such as function application and subscripting bind more tightly than any of the arithmetic operations.

The precedence and associativity determine how expressions are interpreted. Because addition is left associative, the expression a + b + c is interpreted as (a + b) + c. Similarly, a / b * c is interpreted as (a / b) * c.

Because multiplication has higher precedence than addition, the expression a * b + c is interpreted as (a * b) + c and the expression a + b * c is interpreted as a + (b * c). Similarly, 2 * x + 3 * - y is interpreted as (2 * x) + (3 * (-y)).

Transposition and exponentiation bind more tightly than any other arithmetic or logical operation. For vectors, row vectors, and matrices, -u' is interpreted as -(u'), u * v' as u* (v'), and u' * v as (u') * v. For integer and reals, -n ^ 3 is interpreted as -(n ^ 3).

Conditional operator

Conditional operator syntax

The ternary conditional operator is unique in that it takes three arguments and uses a mixed syntax. If a is an expression of type int and b and c are expressions that can be converted to one another (e.g., compared with ==), then

a ? b : c

is an expression of the promoted type of b and c. The only promotion allowed in Stan is integer -> real -> complex; e.g. if one argument is of type int and the other of type real, the conditional expression as a whole is of type real. In other cases, the arguments have to be of the same underlying Stan type (i.e., constraints don’t count, only the shape) and the conditional expression is of that type.

Conditional operator precedence

The conditional operator is the most loosely binding operator, so its arguments rarely require parentheses for disambiguation. For example,

a > 0 || b < 0 ? c + d : e - f

is equivalent to the explicitly grouped version

(a > 0 || b < 0) ? (c + d) : (e - f)

The latter is easier to read even if the parentheses are not strictly necessary.

Conditional operator associativity

The conditional operator is right associative, so that

a ? b : c ? d : e

parses as if explicitly grouped as

a ? b : (c ? d : e)

Again, the explicitly grouped version is easier to read.

Conditional operator semantics

Stan’s conditional operator works very much like its C++ analogue. The first argument must be an expression denoting an integer. Typically this is a variable or a relation operator, as in the variable a in the example above. Then there are two resulting arguments, the first being the result returned if the condition evaluates to true (i.e., non-zero) and the second if the condition evaluates to false (i.e., zero). In the example above, the value b is returned if the condition evaluates to a non-zero value and c is returned if the condition evaluates to zero.

Lazy evaluation of results

The key property of the conditional operator that makes it so useful in high-performance computing is that it only evaluates the returned subexpression, not the alternative expression. In other words, it is not like a typical function that evaluates its argument expressions eagerly in order to pass their values to the function. As usual, the saving is mostly in the derivatives that do not get computed rather than the unnecessary function evaluation itself.

Promotion to parameter

If one return expression is a data value (an expression involving only constants and variables defined in the data or transformed data block), and the other is not, then the ternary operator will promote the data value to a parameter value. This can cause needless work calculating derivatives in some cases and be less efficient than a full if-then conditional statement. For example,

data {
  array[10] real x;
  // ...
parameters {
  array[10] real z;
  // ...
model {
  y ~ normal(cond ? x : z, sigma);
  // ...

would be more efficiently (if not more transparently) coded as

if (cond) {
  y ~ normal(x, sigma);
} else {
  y ~ normal(z, sigma);

The conditional statement, like the conditional operator, only evaluates one of the result statements. In this case, the variable x will not be promoted to a parameter and thus not cause any needless work to be carried out when propagating the chain rule during derivative calculations.


Stan arrays, matrices, vectors, and row vectors are all accessed using the same array-like notation. For instance, if x is a variable of type array [] real (a one-dimensional array of reals) then x[1] is the value of the first element of the array.

Subscripting has higher precedence than any of the arithmetic operations. For example, alpha * x[1] is equivalent to alpha * (x[1]).

Multiple subscripts may be provided within a single pair of square brackets. If x is of type array[,] real, a two-dimensional array, then x[2, 501] is of type real.

Accessing subarrays

The subscripting operator also returns subarrays of arrays. For example, if x is of type array[,,] real, then x[2] is of type array[,] real, and x[2, 3] is of type array[] real. As a result, the expressions x[2, 3] and x[2][3] have the same meaning.

Accessing matrix rows

If Sigma is a variable of type matrix, then Sigma[1] denotes the first row of Sigma and has the type row_vector.

Mixing array and vector/matrix indexes

Stan supports mixed indexing of arrays and their vector, row vector or matrix values. For example, if m is of type matrix[ , ], a two-dimensional array of matrices, then m[1] refers to the first row of the array, which is a one-dimensional array of matrices. More than one index may be used, so that m[1, 2] is of type matrix and denotes the matrix in the first row and second column of the array. Continuing to add indices, m[1, 2, 3] is of type row_vector and denotes the third row of the matrix denoted by m[1, 2]. Finally, m[1, 2, 3, 4] is of type real and denotes the value in the third row and fourth column of the matrix that is found at the first row and second column of the array m.

Multiple indexing and range indexing

In addition to single integer indexes, as described in the language indexing section, Stan supports multiple indexing. Multiple indexes can be integer arrays of indexes, lower bounds, upper bounds, lower and upper bounds, or simply shorthand for all of the indexes. A complete table of index types is given in the following table.

Indexing Options Table

Types of indexes and examples with one-dimensional containers of size N and an integer array ii of type array [] real size K.

index type example value
integer a[11] value of a at index 11
integer array a[ii] a[ii[1]], …, a[ii[K]]
lower bound a[3:] a[3], …, a[N]
upper bound a[:5] a[1], …, a[5]
range a[2:7] a[2], …, a[7]
all a[:] a[1], …, a[N]
all a[] a[1], …, a[N]

Multiple index semantics

The fundamental semantic rule for dealing with multiple indexes is the following. If idxs is a multiple index, then it produces an indexable position in the result. To evaluate that index position in the result, the index is first passed to the multiple index, and the resulting index used.

a[idxs, ...][i, ...] = a[idxs[i], ...][...]

On the other hand, if idx is a single index, it reduces the dimensionality of the output, so that

a[idx, ...] = a[idx][...]

The only issue is what happens with matrices and vectors. Vectors work just like arrays. Matrices with multiple row indexes and multiple column indexes produce matrices. Matrices with multiple row indexes and a single column index become (column) vectors. Matrices with a single row index and multiple column indexes become row vectors. The types are summarized in the following table.

Matrix Indexing Table

Special rules for reducing matrices based on whether the argument is a single or multiple index. Examples are for a matrix a, with integer single indexes i and j and integer array multiple indexes is and js. The same typing rules apply for all multiple indexes.

example row index column index result type
a[i] single n/a row vector
a[is] multiple n/a matrix
a[i, j] single single real
a[i, js] single multiple row vector
a[is, j] multiple single vector
a[is, js] multiple multiple matrix

Evaluation of matrices with multiple indexes is defined to respect the following distributivity conditions.

m[idxs1, idxs2][i, j] = m[idxs1[i], idxs2[j]]
m[idxs, idx][j] = m[idxs[j], idx]
m[idx, idxs][j] = m[idx, idxs[j]]

Evaluation of arrays of matrices and arrays of vectors or row vectors is defined recursively, beginning with the array dimensions.

Function application

Stan provides a range of built in mathematical and statistical functions, which are documented in the built-in function documentation.

Expressions in Stan may consist of the name of function followed by a sequence of zero or more argument expressions. For instance, log(2.0) is the expression of type real denoting the result of applying the natural logarithm to the value of the real literal 2.0.

Syntactically, function application has higher precedence than any of the other operators, so that y + log(x) is interpreted as y + (log(x)).

Type signatures and result type inference

Each function has a type signature which determines the allowable type of its arguments and its return type. For instance, the function signature for the logarithm function can be expressed as

real log(real);

and the signature for the lmultiply function is

real lmultiply(real, real);

A function is uniquely determined by its name and its sequence of argument types. For instance, the following two functions are different functions.

real mean(array [] real);

real mean(vector);

The first applies to a one-dimensional array of real values and the second to a vector.

The identity conditions for functions explicitly forbids having two functions with the same name and argument types but different return types. This restriction also makes it possible to infer the type of a function expression compositionally by only examining the type of its subexpressions.


Constants in Stan are nothing more than nullary (no-argument) functions. For instance, the mathematical constants \(\pi\) and \(e\) are represented as nullary functions named pi() and e(). See the Stan Functions Reference built-in constants section for a list of built-in constants.

Type promotion and function resolution

Because of integer to real type promotion, rules must be established for which function is called given a sequence of argument types. The scheme employed by Stan is the same as that used by C++, which resolves a function call to the function requiring the minimum number of type promotions.

For example, consider a situation in which the following two function signatures have been registered for foo.

real foo(real, real);
int foo(int, int);

The use of foo in the expression foo(1.0, 1.0) resolves to foo(real, real), and thus the expression foo(1.0, 1.0) itself is assigned a type of real.

Because integers may be promoted to real values, the expression foo(1, 1) could potentially match either foo(real, real) or foo(int, int). The former requires two type promotions and the latter requires none, so foo(1, 1) is resolved to function foo(int, int) and is thus assigned the type int.

The expression foo(1, 1.0) has argument types (int, real) and thus does not explicitly match either function signature. By promoting the integer expression 1 to type real, it is able to match foo(real, real), and hence the type of the function expression foo(1, 1.0) is real.

In some cases (though not for any built-in Stan functions), a situation may arise in which the function referred to by an expression remains ambiguous. For example, consider a situation in which there are exactly two functions named bar with the following signatures.

real bar(real, int);
real bar(int, real);

With these signatures, the expression bar(1.0, 1) and bar(1, 1.0) resolve to the first and second of the above functions, respectively. The expression bar(1.0, 1.0) is illegal because real values may not be demoted to integers. The expression bar(1, 1) is illegal for a different reason. If the first argument is promoted to a real value, it matches the first signature, whereas if the second argument is promoted to a real value, it matches the second signature. The problem is that these both require one promotion, so the function name bar is ambiguous. If there is not a unique function requiring fewer promotions than all others, as with bar(1, 1) given the two declarations above, the Stan compiler will flag the expression as illegal.

Random-number generating functions

For most of the distributions supported by Stan, there is a corresponding random-number generating function. These random number generators are named by the distribution with the suffix _rng. For example, a univariate normal random number can be generated by normal_rng(0, 1); only the parameters of the distribution, here a location (0) and scale (1) are specified because the variate is generated.

Random-number generators locations

The use of random-number generating functions is restricted to the transformed data and generated quantities blocks; attempts to use them elsewhere will result in a parsing error with a diagnostic message. They may also be used in the bodies of user-defined functions whose names end in _rng.

This allows the random number generating functions to be used for simulation in general, and for Bayesian posterior predictive checking in particular.

Posterior predictive checking

Posterior predictive checks typically use the parameters of the model to generate simulated data (at the individual and optionally at the group level for hierarchical models), which can then be compared informally using plots and formally by means of test statistics, to the actual data in order to assess the suitability of the model; see Chapter 6 of (Gelman et al. 2013) for more information on posterior predictive checks.

Type inference

Stan is strongly statically typed, meaning that the implementation type of an expression can be resolved at compile time.

Implementation types

The primitive implementation types for Stan are

int, real, complex, vector, row_vector,  matrix, complex_vector,
complex_row_vector, complex_matrix

Every basic declared type corresponds to a primitive type; the following table shows the mapping from types to their primitive types.

Primitive Type Table

The table shows the variable declaration types of Stan and their corresponding primitive implementation type. Stan functions, operators, and probability functions have argument and result types declared in terms of primitive types plus array dimensionality.

type primitive type
int int
real real
vector vector
simplex vector
unit_vector vector
ordered vector
positive_ordered vector
row_vector row_vector
matrix matrix
cov_matrix matrix
corr_matrix matrix
cholesky_factor_cov matrix
cholesky_factor_corr matrix
complex_vector complex_vector
complex_row_vector complex_row_vector
complex_matrix complex_matrix

A full implementation type consists of a primitive implementation type and an integer array dimensionality greater than or equal to zero. These will be written to emphasize their array-like nature. For example, array [] real has an array dimensionality of 1, int an array dimensionality of 0, and array [,,] int an array dimensionality of 3. The implementation type matrix[ , , ] has a total of five dimensions and takes up to five indices, three from the array and two from the matrix.

Recall that the array dimensions come before the matrix or vector dimensions in an expression such as the following declaration of a three-dimensional array of matrices.

array[I, J, K] matrix[M, N] a;

The matrix a is indexed as a[i, j, k, m, n] with the array indices first, followed by the matrix indices, with a[i, j, k] being a matrix and a[i, j, k, m] being a row vector.

Type inference rules

Stan’s type inference rules define the implementation type of an expression based on a background set of variable declarations. The rules work bottom up from primitive literal and variable expressions to complex expressions.


There are two basic promotion rules,

  1. int types may be promoted to real, and
  2. real types may be promoted to complex.

Plus, promotion is transitive, so that

  1. if type U can be promoted to type V and type V can be promoted to type T, then U can be promoted to T.

The first rule means that expressions of type int may be used anywhere an expression of type real is specified, namely in assignment or function argument passing. An integer is promoted to real by casting it in the underlying C++ code.

The remaining rules have to do with covariant typing rules, which say that a container of type U may be promoted to a container of the same shape of type T if U can be promoted to T. For vector and matrix types, this induces three rules,

  1. vector may be promoted to complex_vector,
  2. row_vector may be promoted to complex_row_vector
  3. matrix may be promoted to complex_matrix.

For array types, there’s a single rule

  1. array[...] U may be promoted to array[...] T if U can be promoted to T.

For example, this means array[,] int may be used where array [,] real or array [,] complex is required; as another example, array[] real may be used anywhere array[] complex is required.

Tuples have the natural extension of the above rules, applied to all sub-types at once

  1. A tuple(U1, ..., UN) may be promoted to a tuple(T1, ..., TN) if every Un can be promoted to Tn for n in 1:N


An integer literal expression such as 42 is of type int. Real literals such as 42.0 are of type real. Imaginary literals such as -17i are of type complex. the expression 7 - 2i acts like a complex literal, but technically it combines a real literal 7 and an imaginary literal 2i through subtraction.


The type of a variable declared locally or in a previous block is determined by its declaration. The type of a loop variable is int.

There is always a unique declaration for each variable in each scope because Stan prohibits the redeclaration of an already-declared variables.1


If x is an expression of total dimensionality greater than or equal to \(N\), then the type of expression e[i1, i2, ..., iN] is the same as that of e[i1][i2]...[iN], so it suffices to define the type of a singly-indexed function. Suppose e is an expression and i is an expression of primitive type int. Then

  • if e is an expression of type array[i1, i2, ..., iN] T and k, i1, …, iN are expressions of type int, then e[k] is an expression of type array[i2, ..., iN] T,
  • if e is an expression of type array[i] T with i and k expressions of type int, then e[k] is of type T,
  • if e has implementation type vector or row_vector, dimensionality 0, then e[i] has implementation type real,
  • if e has implementation type matrix, then e[i] has type row_vector,
  • if e has implementation type complex_vector or complex_row_vector and i is an expression of type int, then e[i] is an expression of type complex, and
  • if e has implementation type complex_matrix, and i is an expression of type int, then e[i] is an expression of type complex_row_vector.

Function application

If f is the name of a function and e1,...,eN are expressions for \(N \geq 0\), then f(e1,...,eN) is an expression whose type is determined by the return type in the function signature for f given e1 through eN. Recall that a function signature is a declaration of the argument types and the result type.

In looking up functions, binary operators like real * real are defined as operator*(real, real) in the documentation and index.

In matching a function definition, all of the promotion rules are in play (integers may be promoted to reals, reals to complex, and containers may be promoted if their types are promoted). For example, arguments of type int may be promoted to type real or complex if necessary (see the subsection on type promotion in the function application section, a real argument will be promoted to complex if necessary, a vector will be promoted to complex_vector if necessary, and so on.

In general, matrix operations return the lowest inferable type. For example, row_vector * vector returns a value of type real, which is declared in the function documentation and index as real operator*(row_vector, vector).

Higher-order functions

There are several expression constructions in Stan that act as higher-order functions.2

The higher-order functions and the signature of their argument functions are listed in the following pair of tables.

Higher-order Functions Table

Higher-order functions in Stan with their argument function types. The first group of arguments can be a function of parameters or data. The second group of arguments, consisting of a real and integer array in all cases, must be expressions involving only data and literals.

function parameter or data args data args return type
algebra_solver vector, vector array [] real, array [] real vector
algebra_solver_newton vector, vector array [] real, array [] real vector
integrate_1d, real, real, array [] real array [] real, array [] real real
integrate_ode_X, real, array [] real, array [] real array [] real, array [] real array [] real
map_rect vector, vector array [] real, array [] real vector

For example, the integrate_ode_rk45 function can be used to integrate differential equations in Stan:

functions {
  array [] real foo(real t,
                    array [] real y,
                    array [] real theta,
                    array [] real x_r,
                    array [] real x_i) {
    // ...
// ...
int<lower=1> T;
array[2] real y0;
real t0;
array[T] real ts;
array[1] real theta;
array[0] real x_r;
array[0] int x_i;
// ...
array[T, 2] real y_hat = integrate_ode_rk45(foo, y0, t0,
                                              ts, theta, x_r, x_i);

The function argument is foo, the name of the user-defined function; as shown in the higher-order functions table, integrate_ode_rk45 takes a real array, a real, three more real arrays, and an integer array as arguments and returns 2D real array.

Variadic Higher-order Functions Table

Variadic Higher-order functions in Stan with their argument function types. The first group of arguments are restricted in type. The sequence of trailing arguments can be of any length with any types.

function restricted args return type
solve_X vector vector
ode_X, vector, real, array [] real vector[]
reduce_sum array[] T, T1, T2 real

T, T1, and T2 can be any Stan type.

For example, the ode_rk45 function can be used to integrate differential equations in Stan:

functions {
  vector foo(real t, vector y, real theta, vector beta,
            array [] real x_i, int index) {
    // ...
// ...
int<lower=1> T;
vector[2] y0;
real t0;
array[T] real ts;
real theta;
vector[7] beta;
array[10] int x_i;
int index;
// ...
vector[2] y_hat[T] = ode_rk45(foo, y0, t0, ts, theta,
                              beta, x_i, index);

The function argument is foo, the name of the user-defined function. As shown in the variadic higher-order functions table, ode_rk45 takes a real, a vector, a real, a real array, and a sequence of arguments whose types match those at the end of foo and returns an array of vectors.

Functions passed by reference

The function argument to higher-order functions is always passed as the first argument. This function argument must be provided as the name of a user-defined or built-in function. No quotes are necessary.

Data-restricted arguments

Some of the arguments to higher-order functions are restricted to data. This means they must be expressions containing only data variables, transformed data variables, or literals; the may contain arbitrary functions applied to data variables or literals, but must not contain parameters, transformed parameters, or local variables from any block other than transformed data.

For user-defined functions the qualifier data may be prepended to the type to restrict the argument to data-only variables.

Chain rule and derivatives

Derivatives of the log probability function defined by a model are used in several ways by Stan. The Hamiltonian Monte Carlo samplers, including NUTS, use gradients to guide updates. The BFGS optimizers also use gradients to guide search for posterior modes.

Errors due to chain rule

Unlike evaluations in pure mathematics, evaluation of derivatives in Stan is done by applying the chain rule on an expression-by-expression basis, evaluating using floating-point arithmetic. As a result, models such as the following are problematic for inference involving derivatives.

parameters {
  real x;
model {
  x ~ normal(sqrt(x - x), 1);

Algebraically, statements.qmd#distribution-statements.section in the model could be reduced to

  x ~ normal(0, 1);

and it would seem the model should produce unit normal draws for x. But rather than canceling, the expression sqrt(x - x) causes a problem for derivatives. The cause is the mechanistic evaluation of the chain rule,

\[ \begin{array}{rcl} \frac{d}{dx} \sqrt{x - x} & = & \frac{1}{2 \sqrt{x - x}} \times \frac{d}{dx} (x - x) \\[4pt] & = & \frac{1}{0} \times (1 - 1) \\[4pt] & = & \infty \times 0 \\[4pt] & = & \mathrm{NaN}. \end{array} \]

Rather than the \(x - x\) canceling out, it introduces a 0 into the numerator and denominator of the chain-rule evaluation.

The only way to avoid this kind problem is to be careful to do the necessary algebraic reductions as part of the model and not introduce expressions like sqrt(x - x) for which the chain rule produces not-a-number values.

Diagnosing problems with derivatives

The best way to diagnose whether something is going wrong with the derivatives is to use the test-gradient option to the sampler or optimizer inputs; this option is available in both Stan and RStan (though it may be slow, because it relies on finite differences to make a comparison to the built-in automatic differentiation).

For example, compiling the above model to an executable sqrt-x-minus-x in CmdStan, the test can be run as

> ./sqrt-x-minus-x diagnose test=gradient

which produces


 Log probability=-0.393734

 param idx           value           model     finite diff           error
         0       -0.887393             nan               0             nan

Even though finite differences calculates the right gradient of 0, automatic differentiation follows the chain rule and produces a not-a-number output.

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Gelman, Andrew, J. B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis. Third Edition. London: Chapman & Hall / CRC Press.


  1. Languages such as C++ and R allow the declaration of a variable of a given name in a narrower scope to hide (take precedence over for evaluation) a variable defined in a containing scope.↩︎

  2. Internally, they are implemented as their own expression types because Stan doesn’t have object-level functional types (yet).↩︎