Temporal Dynamics of the Brain Using Variational Bayes Hidden Markov Models: Application in Autism

2019 ◽  
pp. 121-130
Author(s):  
Preetam Srikar Dammu ◽  
Raju Surampudi Bapi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Temporal Dynamics ◽  
Hidden Markov ◽  
Variational Bayes ◽  
The Brain

scholarly journals A Structural Parametrization of the Brain Using Hidden Markov Models-Based Paths in Alzheimer’s Disease

2016 ◽  
Vol 26 (07) ◽  
pp. 1650024 ◽  
Author(s):  
Francisco J. Martinez-Murcia ◽  
Juan M. Górriz ◽  
Javier Ramírez ◽  
Andres Ortiz
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Feature Reduction ◽  
Significant Feature ◽  
Computational Techniques ◽  
Occurrence Matrix ◽  
The Brain

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer’s disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer’s disease neuroimaging initiative (ADNI).

scholarly journals Hidden Markov models and their applications in finance

2015 ◽  
Author(s):  
Αναστάσιος Πετρόπουλος
Keyword(s):  
Hidden Markov Models ◽  
Financial Institutions ◽  
Markov Models ◽  
Temporal Dynamics ◽  
Hidden Markov ◽  
Credit Scoring ◽  
Change Over Time ◽  
Corporate Credit ◽  
Over Time ◽  
Temporal Dependencies

Hidden Markov Models, usually referred to as HMMs, are one of the most successful concepts in statistical modeling conceived and analyzed in the last 40 years. They belong to the stochastic mixture models family and have been broadly implemented in numerous sectors to address the problem of data model fitting and forecasting. Their structure usually is comprised by an observed sequence which is conditioned on an underlying hidden (unobserved) process. This way HMMs provide flexibility to address various complicated problems and can be implemented for modeling univariate and multivariate financial time series. Moreover, based on current literature, economic variables exhibit patterns dependent on different economic regimes which can be successfully captured by HMMs. Their parsimonious structure and attractive properties along with the existence of efficient algorithms for their estimation were the main drivers for the selection of HMM as the main topic of this thesis. Consequently, in this thesis we thoroughly investigate HMMs and their capabilities to simulate financial systems. The contribution of this study is threefold: First we perform an extensive review of HMM theory and applications. Our aim is to summarize the most significant applications of HMM with special focus in the field of finance. We offer a thorough and compact summary of the uses and the results of HMM in the last 40 years. Secondly, we extend the framework of HMMs by proposing a theoretical variation, injecting greater flexibility in their structure. Based on bibliography, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. We address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed financial data. Finally in the third part of this thesis we investigate the HMM efficiency in the problem of corporate credit scoring. We propose a novel corporate credit rating system based on Student’s-t hidden Markov models (SHMMs). Corporate credit scoring is widely used by financial institutions for portfolio risk management, and for pricing financial products designed for corporations. In addition, from a regulatory perspective, internal rating models are commonly used for establishing a more risk-sensitive capital adequacy framework for financial institutions. We evaluate our method against other state of the art statistical techniques like Neural Networks, SVM, and logistic regression and conclude that SHMM offer significant improved forecasting capabilities.

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.

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