nips nips2009 nips2009-188 nips2009-188-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Samuel Gershman, Ed Vul, Joshua B. Tenenbaum
Abstract: While many perceptual and cognitive phenomena are well described in terms of Bayesian inference, the necessary computations are intractable at the scale of realworld tasks, and it remains unclear how the human mind approximates Bayesian computations algorithmically. We explore the proposal that for some tasks, humans use a form of Markov Chain Monte Carlo to approximate the posterior distribution over hidden variables. As a case study, we show how several phenomena of perceptual multistability can be explained as MCMC inference in simple graphical models for low-level vision. 1
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