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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates\054 Inc\056)
/Language (en\055US)
/Created (2016)
/EventType (Poster)
/Description-Abstract (Markov chain Monte Carlo \050MCMC\051 is one of the main workhorses of probabilistic inference\054 but it is notoriously hard to measure the quality of approximate posterior samples\056 This challenge is particularly salient in black box inference methods\054 which can hide details and obscure inference failures\056 In this work\054 we extend the recently introduced bidirectional Monte Carlo technique to evaluate MCMC\055based posterior inference algorithms\056 By running annealed importance sampling \050AIS\051 chains both from prior to posterior and vice versa on simulated data\054 we upper bound in expectation the symmetrized KL divergence between the true posterior distribution and the distribution of approximate samples\056 We integrate our method into two probabilistic programming languages\054 WebPPL and Stan\054 and validate it on several models and datasets\056 As an example of how our method be used to guide the design of inference algorithms\054 we apply it to study the effectiveness of different model representations in WebPPL and Stan\056)
/Producer (PyPDF2)
/Title (Measuring the reliability of MCMC inference with bidirectional Monte Carlo)
/Date (2016)
/ModDate (D\07220170112144609\05508\04700\047)
/Published (2016)
/Type (Conference Proceedings)
/firstpage (2451)
/Book (Advances in Neural Information Processing Systems 29)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (D\056D\056 Lee and M\056 Sugiyama and U\056V\056 Luxburg and I\056 Guyon and R\056 Garnett)
/Author (Roger B\056 Grosse\054 Siddharth Ancha\054 Daniel M\056 Roy)
/lastpage (2459)
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