The State of the Art of Hidden Markov Models for Predictive Maintenance of Diesel Engines

2017 ◽  
Vol 33 (8) ◽  
pp. 2765-2779 ◽  
Author(s):  
António Simões ◽  
José Manuel Viegas ◽  
José Torres Farinha ◽  
Inácio Fonseca
Keyword(s):  
Hidden Markov Models ◽  
Diesel Engines ◽  
Markov Models ◽  
State Of The Art ◽  
Hidden Markov ◽  
The State ◽  
Predictive Maintenance

scholarly journals Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

2015 ◽  
Author(s):  
John Wiedenhoeft ◽  
Eric Brugel ◽  
Alexander Schliep
Keyword(s):  
Hidden Markov Models ◽  
Copy Number ◽  
Markov Models ◽  
State Of The Art ◽  
Hidden Markov ◽  
Copy Number Variants ◽  
Computational Effort ◽  
The State ◽  
Haar Wavelets ◽  
Link Type

AbstractBy combining Haar wavelets with Bayesian Hidden Markov Models, we improve detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. At the same time, we achieve drastically reduced running times, as the method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://bioinformatics.rutgers.edu/Software/HaMMLET/. The web supplement is at http://bioinformatics.rutgers.edu/Supplements/HaMMLET/.Author SummaryIdentifying large-scale genome deletions and duplications, or copy number variants (CNV), accurately in populations or individual patients is a crucial step in indicating disease factors or diagnosing an individual patient's disease type. Hidden Markov Models (HMM) are a type of statistical model widely used for CNV detection, as well as other biological applications such as the analysis of gene expression time course data or the analysis of discrete-valued DNA and protein sequences.As with many statistical models, there are two fundamentally different inference approaches. In the frequentist framework, a single estimate of the model parameters would be used as a basis for subsequent inference, making the identification of CNV dependent on the quality of that estimate. This is an acute problem for HMM as methods for finding globally optimal parameters are not known. Alternatively, one can use a Bayesian approach and integrate over all possible parameter choices. While the latter is known to lead to significantly better results, the much—up to hundreds of times—larger computational effort prevents wide adaptation so far.Our proposed method addresses this by combining Haar wavelets and HMM. We greatly accelerate fully Bayesian HMMs, while simultaneously increasing convergence and thus the accuracy of the Gibbs sampler used for Bayesian computations, leading to substantial improvements over the state-of-the-art.

scholarly journals A non-parametric hidden Markov model for climate state identification

2003 ◽  
Vol 7 (5) ◽  
pp. 652-667 ◽  
Author(s):  
M. F. Lambert ◽  
J. P. Whiting ◽  
A. V. Metcalfe
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Probability Distributions ◽  
Parametric Model ◽  
The State ◽  
Conditional Distributions ◽  
Wet And Dry Cycles ◽  
Non Parametric

Abstract. Hidden Markov models (HMMs) can allow for the varying wet and dry cycles in the climate without the need to simulate supplementary climate variables. The fitting of a parametric HMM relies upon assumptions for the state conditional distributions. It is shown that inappropriate assumptions about state conditional distributions can lead to biased estimates of state transition probabilities. An alternative non-parametric model with a hidden state structure that overcomes this problem is described. It is shown that a two-state non-parametric model produces accurate estimates of both transition probabilities and the state conditional distributions. The non-parametric model can be used directly or as a technique for identifying appropriate state conditional distributions to apply when fitting a parametric HMM. The non-parametric model is fitted to data from ten rainfall stations and four streamflow gauging stations at varying distances inland from the Pacific coast of Australia. Evidence for hydrological persistence, though not mathematical persistence, was identified in both rainfall and streamflow records, with the latter showing hidden states with longer sojourn times. Persistence appears to increase with distance from the coast. Keywords: Hidden Markov models, non-parametric, two-state model, climate states, persistence, probability distributions

HMM with emission process resulting from a special combination of independent Markovian emissions

2017 ◽  
Vol 23 (4) ◽  
Author(s):  
Abdelaziz Nasroallah ◽  
Karima Elkimakh
Keyword(s):  
Markov Chains ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
The State ◽  
State Spaces ◽  
Standard Case ◽  
Emission Process

AbstractOne of the most used variants of hidden Markov models (HMMs) is the standard case where the time is discrete and the state spaces (hidden and observed spaces) are finite. In this framework, we are interested in HMMs whose emission process results from a combination of independent Markov chains. Principally, we assume that the emission process evolves as follows: given a hidden state realization

scholarly journals An Efficient Algorithm for Estimating State Sequences in Imprecise Hidden Markov Models

2014 ◽  
Vol 50 ◽  
pp. 189-233 ◽  
Author(s):  
J. De Bock ◽  
G. De Cooman
Keyword(s):  
Hidden Markov Models ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Exact Algorithm ◽  
Mass Function ◽  
The State ◽  
Model Parameters ◽  
State Sequence

We present an efficient exact algorithm for estimating state sequences from outputs or observations in imprecise hidden Markov models (iHMMs). The uncertainty linking one state to the next, and that linking a state to its output, is represented by a set of probability mass functions instead of a single such mass function. We consider as best estimates for state sequences the maximal sequences for the posterior joint state model conditioned on the observed output sequence, associated with a gain function that is the indicator of the state sequence. This corresponds to and generalises finding the state sequence with the highest posterior probability in (precise-probabilistic) HMMs, thereby making our algorithm a generalisation of the one by Viterbi. We argue that the computational complexity of our algorithm is at worst quadratic in the length of the iHMM, cubic in the number of states, and essentially linear in the number of maximal state sequences. An important feature of our imprecise approach is that there may be more than one maximal sequence, typically in those instances where its precise-probabilistic counterpart is sensitive to the choice of prior. For binary iHMMs, we investigate experimentally how the number of maximal state sequences depends on the model parameters. We also present an application in optical character recognition, demonstrating that our algorithm can be usefully applied to robustify the inferences made by its precise-probabilistic counterpart.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

Sign in / Sign up

Export Citation Format

Share Document