Approximate Bayesian Computation for Smoothing

作者：Martin James S; Jasra Ajay^*; Singh Sumeetpal S; Whiteley Nick; Del Moral Pierre; McCoy Emma

来源：Stochastic Analysis and Applications, 2014, 32(3): 397-420.

DOI：10.1080/07362994.2013.879262

• Approximate Bayesian Computation
• Smoothing
• Hidden Markov models
• Sequential Monte Carlo

We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the observations. The quality of the approximation may be controlled to arbitrary precision through a parameter epsilon > 0. We provide theoretical results which quantify, in terms of epsilon, the ABC error in approximation of expectations of additive functionals with respect to the smoothing distributions. Under regularity assumptions, this error is , where n is the number of time steps over which smoothing is performed. For numerical implementation, we adopt the forward-only sequential Monte Carlo (SMC) scheme of [14] and quantify the combined error from the ABC and SMC approximations. This forms some of the first quantitative results for ABC methods which jointly treat the ABC and simulation errors, with a finite number of data and simulated samples.

• 出版日期2014-5-4