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Approximation of the Logistic Function by the Normal CDF in the Stochastic Metropolis-Hastings

Student: Andrey Chernov

Supervisor: Dmitry Vetrov

Faculty: Faculty of Computer Science

Educational Programme: Data Science (Master)

Year of Graduation: 2021

The state of the art models have intractable likelihood. So we can not compute posterior distribution over weights analytically, but using Markov chain monte carlo framework (MCMC) we can approximate it. The most popular MCMC methods use Metropolis-Hastings (MH) acceptance test to get unbiased estimator of the posterior distribution. MH acceptance test re- quires iterate over full dataset on each step. Thus, the training procedure of the MCMC methods is time-consuming. In this paper we will propose subsampling MH acceptance test.

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