The `$lp()`

method extracts `lp__`

, the total log probability
(`target`

) accumulated in the `model`

block of the Stan program. For
variational inference the log density of the variational approximation to
the posterior is also available via the `$lp_approx()`

method.

See the Log Probability Increment vs. Sampling Statement
section of the Stan Reference Manual for details on when normalizing
constants are dropped from log probability calculations.

lp()
lp_approx()

## Value

A numeric vector with length equal to the number of (post-warmup)
draws for MCMC and variational inference, and length equal to `1`

for
optimization.

## Details

`lp__`

is the unnormalized log density on Stan's unconstrained space.
This will in general be different than the unnormalized model log density
evaluated at a posterior draw (which is on the constrained space). `lp__`

is
intended to diagnose sampling efficiency and evaluate approximations.

`lp_approx__`

is the log density of the variational approximation to `lp__`

(also on the unconstrained space). It is exposed in the variational method
for performing the checks described in Yao et al. (2018) and implemented in
the loo package.

## References

Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018). Yes, but did it
work?: Evaluating variational inference. *Proceedings of the 35th
International Conference on Machine Learning*, PMLR 80:5581–5590.

## See also

## Examples

#> Model executable is up to date!

#> [1] -67.0068 -67.0346 -67.1231 -65.7535 -65.5887 -65.4090

#> Model executable is up to date!

fit_mle$lp()

#> [1] -63.9218

#> Model executable is up to date!

# }