A surrogate likelihood as a plain R function

Fixes an observation and returns function(theta), so the learned likelihood can be handed to anything in R that wants a log-density: stats::optim(), an MCMC package, an importance sampler, a profile likelihood. The returned function is vectorized – pass a matrix of parameter values and get one log-likelihood per row.

Usage

likelihood_fn(fit, x_obs, ...)

Arguments

fit

An nsbi_nle fit from nle().

x_obs

The observation to condition on. Rows are independent observations; the log-likelihood sums over them.

...

Passed to log_lik().

Value

A function of theta returning a numeric vector of log-likelihoods, with the observation attached as attribute x_obs.

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")
#> Running the simulator sequentially. To use all your cores:
#>   library(future)
#>   plan(multisession)
#> Hide this hint with options(neuralsbi.parallel_hint = FALSE).

loglik <- likelihood_fn(fit, matrix(rnorm(20, 1, 0.5), ncol = 1))
optimize(function(m) loglik(m), c(-3, 3), maximum = TRUE)$maximum
#> [1] 0.9550394