• Corpus ID: 62082134

Hidden Markov Models: Estimation and Control

@inproceedings{Elliott1994HiddenMM,
  title={Hidden Markov Models: Estimation and Control},
  author={Robert J R Elliott and Lakhdar Aggoun and John B. Moore},
  year={1994}
}
Hidden Markov Model Processing.- Discrete-Time HMM Estimation.- Discrete States and Discrete Observations.- Continuous-Range Observations.- Continuous-Range States and Observations.- A General Recursive Filter.- Practical Recursive Filters.- Continuous-Time HMM Estimation.- Discrete-Range States and Observations.- Markov Chains in Brownian Motion.- Two-Dimensional HMM Estimation.- Hidden Markov Random Fields.- HMM Optimal Control.- Discrete-Time HMM Control.- Risk-Sensitive Control of HMM… 

Hidden Markov model state estimation with randomly delayed observations

This paper considers state estimation for a discrete-time hidden Markov model (HMM) when the observations are delayed by a random time. The delay process is itself modeled as a finite state Markov

An Expectation-Maximization Algorithm for Continuous-time Hidden Markov Models

We propose a unified framework that extends the inference methods for classical hidden Markov models to continuous settings, where both the hidden states and observations occur in continuous time.

Some results on ergodic and adaptive control of hidden Markov models

The dynamics of a discrete time, state process are assumed to depend on the current value of the state of a possibly unobserved hidden Markov model. Both the state and the hidden process are

Parameter estimation of Gaussian hidden Markov models when missing observations occur

TLDR
The basic Gaussian Hidden Markov model is introduced and some joint probability density functions of the process are presented, some of which are shown below.

Subspace estimation and prediction methods for hidden Markov models

TLDR
This paper examines subspace estimation methods for HMMs whose output lies a finite set, and shows that the estimates of the transition and emission probability matrices are consistent up to a similarity transformation, and that the m-step linear predictor computed from the estimated system matrices is consistent, i.e., converges to the true optimal linear m- step predictor.

Causal Recursive Parameter Estimation for Discrete-Time Hidden Bivariate Markov Chains

TLDR
An algorithm for causal recursive parameter estimation of a discrete-time hidden bivariate Markov chain is developed and the performance of the algorithm is demonstrated in estimating the model's parameter and its sojourn time distribution in a numerical example.

Recursive estimation of multivariate hidden Markov model parameters

TLDR
The properties of the proposed recursive expectation–maximization (EM) algorithm were explored by a computer simulation solving test examples and demonstrate that this algorithm can be efficiently applied to solve online tasks related to HMM parameter estimation.

Optimal Control of Hidden Markov Models With Binary Observations

TLDR
A nonlinear state-space representation for the estimator, analytical expressions for the control law are developed, and numerical methods for efficient computation of the optimal control are presented.

Exact and approximate hidden Markov chain filters based on discrete observations

TLDR
This paper derives exact formulas for the necessary densities in the case the state space of the HMM consists of two elements only, by relating the underlying integrated continuous-time Markov chain to the so-called asymmetric telegraph process and by using recent results on this process.
...