log_lik() evaluates the likelihood learned by nle():
\(\log q_\phi(x \mid \theta)\), in the original units of both.
Usage
log_lik(fit, theta, x, ...)
# S3 method for class 'nsbi_nle'
log_lik(fit, theta, x, sum_iid = TRUE, max_batch = 1e+05, ...)Arguments
- fit
An
nsbi_nlefit fromnle().- theta
Parameter values: a numeric vector (one parameter set) or an
n_theta x dim_thetamatrix.- x
Observed data: a numeric vector (one observation) or an
n_obs x dim_xmatrix whose rows are independent observations.- ...
Unused, for S3 consistency.
- sum_iid
Sum the log-density over the rows of
x(the default). SetFALSEto get the per-observation values instead.- max_batch
Largest number of
(theta, x)pairs evaluated in one call to the estimator. Only affects memory and speed.
Value
With sum_iid = TRUE, a numeric vector with one entry per row of
theta. With sum_iid = FALSE, an n_theta x n_obs matrix.
Details
Rows of x are treated as independent observations from the same
parameter, so by default the result sums over them,
\(\sum_i \log q_\phi(x_i \mid \theta)\). That sum is the whole point of
NLE: the estimator is trained on one observation at a time, and the number
of observations you condition on afterwards is free.
See also
likelihood_fn() for a closure over a fixed observation,
posterior() to turn the likelihood into posterior draws.
Examples
prior <- prior_uniform(c(mu = -3), c(mu = 3))
fit <- nle(prior, function(mu) c(y = rnorm(1, mu, 0.5)),
n_simulations = 1000, density_estimator = "linear_gaussian")
x_obs <- matrix(rnorm(20, mean = 1, sd = 0.5), ncol = 1)
grid <- matrix(seq(-2, 2, length.out = 5), ncol = 1)
log_lik(fit, grid, x_obs)
#> [1] -400.28858 -185.21681 -55.90868 -12.36420 -54.58337