Statistical Inference in Hidden Markov Models Using k-Segment Constraints by Titsias, M, Holmes, C, Yau, C

“…Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. …”

Computationally intensive methods for Hidden Markov Models with applications to statistical genetics by Kecskemethy, P

“…</p> <p>This work attempts to explore avenues for achieving high performance for Hidden Markov Models (HMMs) and HMM applications in population genetics.…”

A decision theoretic approach for segmental classification using Hidden Markov models. by Yau, C, Holmes, C

“…This paper is concerned with statistical methods for the analysis of linear sequence data using Hidden Markov Models (HMMs) where the task is to segment and classify the data according to the underlying hidden state sequence. …”

Bayesian Nonparametric Hidden Markov Models with application to the analysis of copy-number-variation in mammalian genomes. by Yau, C, Papaspiliopoulos, O, Roberts, G, Holmes, C

“…We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models. Our approach uses a Mixture of Dirichlet Process (MDP) model for the unknown sampling distribution (likelihood) for the observations arising in each state and a computationally efficient data augmentation scheme to aid inference. …”

Bayesian Nonparametric Hidden Markov Models with application to the analysis of copy-number-variation in mammalian genomes. by Yau, C, Papaspiliopoulos, O, Roberts, G, Holmes, C

“…We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models. Our approach uses a Mixture of Dirichlet Process (MDP) model for the unknown sampling distribution (likelihood) for the observations arising in each state and a computationally efficient data augmentation scheme to aid inference. …”

QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data by Colella, S, Yau, C, Taylor, J, Mirza, G, Butler, H, Clouston, P, Bassett, A, Seller, A, Holmes, C, Ragoussis, J

“…We developed, and experimentally validated, a novel computational framework (QuantiSNP) for detecting regions of copy number variation from BeadArray™ SNP genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM). Objective Bayes measures are used to set certain hyperparameters in the priors using a novel re-sampling framework to calibrate the model to a fixed Type I (false positive) error rate. …”

QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. by Colella, S, Yau, C, Taylor, J, Mirza, G, Butler, H, Clouston, P, Bassett, A, Seller, A, Holmes, C, Ragoussis, J

“…We developed, and experimentally validated, a novel computational framework (QuantiSNP) for detecting regions of copy number variation from BeadArray SNP genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM). Objective Bayes measures are used to set certain hyperparameters in the priors using a novel re-sampling framework to calibrate the model to a fixed Type I (false positive) error rate. …”

Phylogenetic inference under recombination using Bayesian stochastic topology selection. by Webb, A, Hancock, J, Holmes, C

“…Here, we generalize a hidden Markov model (HMM) to infer changes in phylogeny along multiple sequence alignments while accounting for rate heterogeneity; the hidden states refer to the unobserved phylogenic topology underlying the relatedness at a genomic location. …”

Statistical methods for mapping complex traits by Allchin, L

“…It was proposed that, for populations descended from a known number of founders, it would be possible to sequence these individuals with a very low coverage, use a hidden Markov model (HMM) to represent the chromosomes as mosaics of the founders, then use these states to impute the missing data. …”

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants by Willetts, M, Hollowell, S, Aslett, L, Holmes, C, Doherty, AR

“…Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. …”

A DECISION-THEORETIC APPROACH FOR SEGMENTAL CLASSIFICATION by Yau, C, Holmes, C

“…The result is generic and applicable to any probabilistic model on a sequence, such as Hidden Markov models, change point or product partition models. © Institute of Mathematical Statistics, 2013.…”