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Matias Quiroz: Speeding Up MCMC With Efficient Data Sub-sampling

Tid: On 2014-04-16 kl 13.00 - 14.00

Plats: Room B705, Department of statistics, Stockholm university

Medverkande: Matias Quiroz, Stockholm university

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The computing time for Markov Chain Monte Carlo (MCMC) algorithms can be prohibitively large for cross-sectional and longitudinal data with many observations, especially when the data density for each observation is costly to evaluate. We propose a framework based on a Pseudo-marginal MCMC where the likelihood function is unbiasedly estimated from a random subset of the data, resulting in substantially fewer density evaluations. The subsets are selected using efficient sampling schemes, such as Probability Proportional-to-Size (PPS) sampling where the inclusion probability of an observation is proportional to an approximation of its contribution to the likelihood function. We illustrate the approach on a large micro-economic dataset of Swedish firms containing half a million observations.