Ensemble hidden Markov models for biosignal analysis by Rezek, I, Roberts, S

Equivalence of hidden Markov models with continuous observations by Darwin, O, Kiefer, SM

“…We consider Hidden Markov Models that emit sequences of observations that are drawn from continuous distributions. …”

Coupled hidden Markov models for biosignal interaction modelling by Rezek, I, Sykacek, P, Roberts, S, IEE, IEE

Collapsed Variational Bayesian Inference for Hidden Markov Models by Wang, P, Blunsom, P

On computing the total variation distance of hidden Markov models by Kiefer, S

“…We prove results on the decidability and complexity of computing the total variation distance (equivalently, the L1-distance) of hidden Markov models (equivalently, labelled Markov chains). …”

Stochastic collapsed variational inference for hidden Markov models by Wang, P, Blunsom, P

“…In this paper, we propose a stochastic collapsed variational inference algorithm for hidden Markov models, in a sequential data setting. Given a collapsed hidden Markov Model, we break its long Markov chain into a set of short subchains. …”

On the sequential probability ratio test in hidden Markov models by Darwin, O, Kiefer, S

“…<p>We consider the Sequential Probability Ratio Test applied to Hidden Markov Models. Given two Hidden Markov Models and a sequence of observations generated by one of them, the Sequential Probability Ratio Test attempts to decide which model produced the sequence. …”

Maximum a Posteriori Estimation of Coupled Hidden Markov Models. by Rezek, I, Gibbs, M, Roberts, S

“…Coupled Hidden Markov Models (CHMM) are a tool which model interactions between variables in state space rather than observation space. …”

Hamming ball auxiliary sampling for factorial hidden Markov models by Yau, C, Titsias, M

“…We introduce a novel sampling algorithm for Markov chain Monte Carlo-based Bayesian inference for factorial hidden Markov models. This algorithm is based on an auxiliary variable construction that restricts the model space allowing iterative exploration in polynomial time. …”

Estimation of Coupled Hidden Markov Models with application to biosignal interaction modelling by Rezek, I, Roberts, S

A new take on detecting insider threats: Exploring the use of hidden Markov Models by Rashid, T, Agrafiotis, I, Nurse, J

“…Specifically, we make use of Hidden Markov Models to learn what constitutes normal behaviour, and then use them to detect significant deviations from that behaviour. …”

Local wall motion classification of stress echocardiography using a Hidden Markov Model approach by Mansor, S, Noble, J, IEEE

Markov models for automated ECG interval analysis by Hughes, N, Tarassenko, L, Roberts, S

“…We show that the state durations implicit in a standard hidden Markov model are ill-suited to those of real ECG features, and we investigate the use of hidden semi-Markov models for improved state duration modelling.…”

A Hierarchical Pitman-Yor Process HMM for Unsupervised Part of Speech Induction. by Blunsom, P, Cohn, T

“…We develop a novel hidden Markov model incorporating sophisticated smoothing using a hierarchical Pitman-Yor processes prior, providing an elegant and principled means of incorporating lexical characteristics. …”

A value of information framework for latent variable models by Wang, Z, Badiu, M-A, Coon, JP

“…Moreover, the VoI expression for a hidden Markov model is obtained in this setting. Numerical results are provided to show the relationship between the VoI and the traditional age of information (AoI) metric, and the VoI of Markov and hidden Markov models are analysed for the particular case when the latent process is an Ornstein-Uhlenbeck process. …”

Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles by Wu, M, Louw, T, Lahijanian, M, Ruan, W, Huang, X, Merat, N, Kwiatkowska, M

“…The method models human intention as the latent states of a hidden Markov model and uses probabilistic dynamic time warping distributions to capture the temporal characteristics of the observation patterns of the drivers. …”

Feature extraction and wall motion classification of 2D stress echocardiography with support vector machines by Chykeyuk, K, Clifton, D, Noble, J

“…One previous attempt [1] has been made to combine information from rest and stress sequences utilising a Hidden Markov Model (HMM), which has proven to be the best performing approach to date. …”

Stochastic collapsed variational inference for sequential data by Wang, P, Blunsom, P

“…Our algorithm is applicable to both finite hidden Markov models and hierarchical Dirichlet process hidden Markov models, and to any datasets generated by emission distributions in the exponential family. …”

Demand driven deep brain stimulation: regimes and autoregressive hidden Markov implementation. by Brittain, J, Probert-Smith, P, Aziz, T

“…Implementation strategies are discussed with a focus on vector-autoregressive hidden Markov models for tremor prediction. Detection of simple motor actions versus tremor are compared in a preliminary performance analysis.…”

Two dissimilarity measures for HMMs and their application in phoneme model clustering by Vihola, M, Harju, M, Salmela, P, Suontausta, J, Savela, J

“…This paper introduces two approximations of the Kullback-Leibler divergence for hidden Markov models (HMMs). The first one is a generalization of an approximation originally presented for HMMs with discrete observation densities. …”