Transdimensional sequential Monte Carlo for hidden Markov models using variational Bayes - SMCVB
Clare McGrory, Daniel Ahfock
DOI: http://dx.doi.org/10.15439/2014F483
Citation: Position Papers of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 3, pages 61–66 (2014)
Abstract. In this paper we outline a transdimensional sequential Monte Carlo algorithm - SMCVB - for fitting hidden Markov models. Sequential Monte Carlo (SMC) involves generating a weighted sample of particles from a sequence of probability distributions with the aim of converging to the target Bayesian posterior distribution. SMCVB makes use of variational Bayes (VB) in combination with SMC principles to create an algorithm which targets the posterior more efficiently thereby saving on time and computational storage requirements. Another key feature of our methodology is that the variational-Bayes-generated proposals can vary in dimension. We have found in our simulation studies that we are able to obtain sensible estimates of the model dimensionality in this one-step procedure. This introduces very valuable additional flexibility in the modelling approach and opens up the potential for use of the algorithm in on-line settings where efficient and reliable estimation of dimensionality and parameters is required.