【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...Deep Learning JP
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The document proposes a method called SUMO (Stochastically Unbiased Marginalization Objective) for estimating log marginal probabilities in latent variable models. SUMO uses a Russian roulette estimator to obtain an unbiased estimate of the log marginal likelihood. This allows SUMO to provide an objective function for variational inference that converges to the log marginal likelihood as more samples are taken, avoiding the bias issues of methods like VAEs and IWAE. The paper outlines SUMO, compares it to existing methods, and demonstrates its effectiveness on density estimation tasks.
The document appears to discuss Bayesian statistical modeling and inference. It includes definitions of terms like the correlation coefficient (ρ), bivariate normal distributions, and binomial distributions. It shows the setup of a Bayesian hierarchical model with multivariate normal outcomes and estimates of the model parameters, including the correlations (ρA and ρB) between two groups of bivariate data.
The document appears to discuss Bayesian statistical modeling and inference. It includes definitions of terms like the correlation coefficient (ρ), bivariate normal distributions, and binomial distributions. It shows the setup of a Bayesian hierarchical model with multivariate normal outcomes and estimates of the model parameters, including the correlations (ρA and ρB) between two groups of bivariate data.