MC-STAN . ORG {
}
Title:
Run Stan's variational algorithm for approximate posterior sampling — vb • rstan
Description:
Approximately draw from a posterior distribution using variational inference. This is still considered an experimental feature. We recommend calling stan or sampling for final inferences and only using vb to get a rough idea of the parameter distributions.
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12 years and 10 months (reg. 2012-08-15).
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Keywords {🔍}
stan, defaulting, integer, data, true, positive, seed, parameters, number, false, variational, stanmodel, include, indicating, samples, algorithm, posterior, fullrank, character, default, logical, inference, pars, init, methods, model, object, scalar, maximum, references, draws, elbo, stepsize, cmdstan, stans, sampling, draw, distribution, parameter, vbobject, list, random, checkdata, samplefile, importanceresampling, keepevery, class, initial, values, arguments,
Topics {✒️}
full-rank covariance matrix monte carlo estimate evidence lower bound recommend calling stan variational bayes methods entire multidimensional parameters fully factorized gaussian avoid dependent samples maximum integer supported integer produces na random number generation posterior distribution s4 class stanmodel variational inference algorithm initial values character vector posterior samples init = 'random' parameter distributions call stan' stan section stan manual //mc-stan variational inference init argument character string check_data logical positive integer stanfit-class variational algorithm optional parameters class stanmodel 24 vignettes functions experimental feature final inferences rough idea importance_resampling = false named list environment providing seeds derived detailed documentation saved quantities additional references importance resampling convergence tolerance relative norm nth iteration reference manual jiqiang guo jonah gabry
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