Stochastic Variational Inference for Hidden Markov Models

11/06/2014
by   Nicholas J. Foti, et al.
0

Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI). However, such methods have largely been studied in independent or exchangeable data settings. We develop an SVI algorithm to learn the parameters of hidden Markov models (HMMs) in a time-dependent data setting. The challenge in applying stochastic optimization in this setting arises from dependencies in the chain, which must be broken to consider minibatches of observations. We propose an algorithm that harnesses the memory decay of the chain to adaptively bound errors arising from edge effects. We demonstrate the effectiveness of our algorithm on synthetic experiments and a large genomics dataset where a batch algorithm is computationally infeasible.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/05/2015

Stochastic Collapsed Variational Inference for Hidden Markov Models

Stochastic variational inference for collapsed models has recently been ...
research
06/14/2017

Stochastic Gradient MCMC Methods for Hidden Markov Models

Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scal...
research
07/05/2022

Stochastic Variational Methods in Generalized Hidden Semi-Markov Models to Characterize Functionality in Random Heteropolymers

Recent years have seen substantial advances in the development of biofun...
research
10/17/2017

Estimate exponential memory decay in Hidden Markov Model and its applications

Inference in hidden Markov model has been challenging in terms of scalab...
research
05/31/2016

Extreme Stochastic Variational Inference: Distributed and Asynchronous

We propose extreme stochastic variational inference (ESVI), an asynchron...
research
02/27/2018

ADMM-based Networked Stochastic Variational Inference

Owing to the recent advances in "Big Data" modeling and prediction tasks...
research
05/20/2021

Scalable Estimation Algorithm for the DINA Q-matrix Combining Stochastic Optimization and Variational Inference

Diagnostic classification models (DCMs) enable finer-grained inspection ...

Please sign up or login with your details

Forgot password? Click here to reset