## 21.4 Model Conditioning and Curvature

Because Stan’s algorithms (other than Riemannian Hamiltonian Monte Carlo) rely on step-based gradient-based approximations of the density (or penalized maximum likelihood) being fitted, posterior curvature not captured by this first-order approximation plays a central role in determining the statistical efficiency of Stan’s algorithms.

A second-order approximation to curvature is provided by the Hessian, the matrix of second derivatives of the log density $$\log p(\theta)$$ with respect to the parameter vector $$\theta$$, defined as $H(\theta) = \nabla \, \nabla \, \log p(\theta | y),$ so that $H_{i, j}(\theta) = \frac{\partial^2 \log p(\theta | y)} {\partial \theta_i \ \partial \theta_j}.$ For pure penalized maximum likelihood problems, the posterior log density $$\log p(\theta | y)$$ is replaced by the penalized likelihood function $$\mathcal{L}(\theta) = \log p(y | \theta) - \lambda(\theta)$$.

A good gauge of how difficult a problem the curvature presents is given by the condition number of the Hessian matrix $$H$$, which is the ratio of the largest to the smallest eigenvalue of $$H$$ (assuming the Hessian is positive definite). This essentially measures the difference between the flattest direction of movement and the most curved. Typically, the step size of a gradient-based algorithm is bounded by the most sharply curved direction. With better conditioned log densities or penalized likelihood functions, it is easier for Stan’s adaptation, especially the diagonal adaptations that are used as defaults.

### Unit Scales without Correlation

Ideally, all parameters should be programmed so that they have unit scale and so that posterior correlation is reduced; together, these properties mean that there is no rotation or scaling required for optimal performance of Stan’s algorithms. For Hamiltonian Monte Carlo, this implies a unit mass matrix, which requires no adaptation as it is where the algorithm initializes. Riemannian Hamiltonian Monte Carlo performs this conditioning on the fly at every step, but such conditioning is expensive computationally.

### Varying Curvature

In all but very simple models (such as multivariate normals), the Hessian will vary as $$\theta$$ varies (an extreme example is Neal’s funnel, as naturally arises in hierarchical models with little or no data). The more the curvature varies, the harder it is for all of the algorithms with fixed adaptation parameters (that is, everything but Riemannian Hamiltonian Monte Carlo) to find adaptations that cover the entire density well. Many of the variable transforms proposed are aimed at improving the conditioning of the Hessian and/or making it more consistent across the relevant portions of the density (or penalized maximum likelihood function) being fit.

For all of Stan’s algorithms, the curvature along the path from the initial values of the parameters to the solution is relevant. For penalized maximum likelihood and variational inference, the solution of the iterative algorithm will be a single point, so this is all that matters. For sampling, the relevant “solution” is the typical set, which is the posterior volume where almost all draws from the posterior lies; thus, the typical set contains almost all of the posterior probability mass.

With sampling, the curvature may vary dramatically between the points on the path from the initialization point to the typical set and within the typical set. This is why adaptation needs to run long enough to visit enough points in the typical set to get a good first-order estimate of the curvature within the typical set. If adaptation is not run long enough, sampling within the typical set after adaptation will not be efficient. We generally recommend at least one hundred iterations after the typical set is reached (and the first effective draw is ready to be realized). Whether adaptation has run long enough can be measured by comparing the adaptation parameters derived from a set of diffuse initial parameter values.

### Reparameterizing with a Change of Variables

Improving statistical efficiency is achieved by reparameterizing the model so that the same result may be calculated using a density or penalized maximum likelihood that is better conditioned. Again, see the example of reparameterizing Neal’s funnel for an example, and also the examples in the change of variables chapter.

One has to be careful in using change-of-variables reparameterizations when using maximum likelihood estimation, because they can change the result if the Jacobian term is inadvertently included in the revised likelihood model.