Stochastic Volatility Models as Hidden Markov Models and Statistical Applications

Stochastic observation hidden Markov models

Carl D. Mitchell 1 Mary P. Harper 2 Leah H. Jamieson 2 1AT&T Bell Laboratories, 600 Mountain Ave., Murray Hill, NJ 07974, [email protected] 2School of Electrical and Computer Engineering, Purdue University West Lafayette, IN 47907-1285, flhj,[email protected] ABSTRACT Hybrids that use a neural network to estimate the output probabilitiy for a hidden Markov model (HMM) word recognizer ha...

Stochastic variational inference for hidden Markov models

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 ...

Hidden Markov Models: Fundamentals and Applications Part 2: Discrete and Continuous Hidden Markov Models

The objective of this tutorial is to introduce basic concepts of a Hidden Markov Model (HMM). The tutorial is intended for the practicing engineer, biologist, linguist or programmer who would like to learn more about the above mentioned fascinating mathematical models and include them into one’s repertoire. This part of the tutorial is devoted to the basic concepts of a Hidden Markov Model. You...

Statistical analysis of neural data: Hidden Markov models and applications to multi-state neural models

On Markov and Hidden Markov Models with Applications to Trajectories