Several Monte Carlo methods have been proposed for computing marginal likelihoods in Bayesian analyses. Some of these involve sampling from a sequence of ...
This can happen in problems involving latent variables whose support depends upon the data and can make some methods inefficient and others invalid. The ...
Computation of marginal likelihoods with data-dependent support for latent variables · S. Heaps, R. Boys, M. Farrow · Published in Computational Statistics… 1 ...
Abstract: Several Monte Carlo methods have been proposed for computing marginal likelihoods in Bayesian analyses. Some of these involve sampling from a sequence ...
Computation of marginal likelihoods with data-dependent support for latent variables. Heaps, Sarah E.; Boys, Richard J.; Farrow, Malcolm.
The estimation often involves maximizing an objective function based on a marginal likelihood/pseudo-likelihood, possibly with constraints and/or penalties on ...
May 7, 2022 · The estimation often involves maximizing an objective function based on a marginal likelihood/pseudo-likelihood, possibly with constraints and/ ...
Computation of marginal likelihoods with data–dependent support for latent variables. Type. D - Journal article. DOI. 10.1016/j.csda.2013.07.033.
Abstract. We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping ...
We show that this unbiased estimator can train latent variable models to achieve higher test log-likelihood than lower bound estimators at the same expected ...