Stochastic Gradient MCMC Methods for Hidden Markov Models

  title={Stochastic Gradient MCMC Methods for Hidden Markov Models},
  author={Yi-An Ma and Nicholas J. Foti and Emily B. Fox},
Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling Bayesian inference to large datasets under an assumption of i.i.d data. We instead develop an SGMCMC algorithm to learn the parameters of hidden Markov models (HMMs) for time-dependent data. There are two challenges to applying SGMCMC in this setting: The latent discrete states, and needing to break dependencies when considering minibatches. We consider a marginal likelihood representation of the HMM and propose an… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 29 references

Bayesian methods for hidden Markov models: Recursive computing in the 21st century

  • S. L. Scott
  • Journal of the American Statistical Association…
  • 2002
Highly Influential
4 Excerpts

A unifying framework for devising efficient and irreversible MCMC samplers

  • Ma, Y.-A, E. B. Fox, T. Chen, L. Wu
  • 2016
3 Excerpts

Similar Papers

Loading similar papers…