scholarly journals A regularized hidden Markov model for analyzing the ‘hot shoe’ in football

2021 ◽  
pp. 1471082X2110080
Author(s):  
Marius Ötting ◽  
Groll Andreas
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Hot Hand ◽  
Special Cases ◽  
Psychology And Economics ◽  
Likelihood Approach ◽  
Penalized Likelihood Approach ◽  
Hot Hand Effect

We propose a penalized likelihood approach in hidden Markov models (HMMs) to perform automated variable selection. To account for a potential large number of covariates, which also may be substantially correlated, we consider the elastic net penalty containing LASSO and ridge as special cases. By quadratically approximating the non-differentiable penalty, we ensure that the likelihood can be maximized numerically. The feasibility of our approach is assessed in simulation experiments. As a case study, we examine the ‘hot hand’ effect, whose existence is highly debated in different fields, such as psychology and economics. In the present work, we investigate a potential ‘hot shoe’ effect for the performance of penalty takers in (association) football, where the (latent) states of the HMM serve for the underlying form of a player.

Using hidden Markov models to find discrete targets in continuous sociophonetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Duncan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Abstract Representation ◽  
Language Variation And Change ◽  
Novel Approach ◽  
Single Target ◽  
The Individual ◽  
Individual Speaker

Abstract Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

scholarly journals Probabilistic Independence Networks for Hidden Markov Probability Models

1997 ◽  
Vol 9 (2) ◽  
pp. 227-269 ◽  
Author(s):  
Padhraic Smyth ◽  
David Heckerman ◽  
Michael I. Jordan
Keyword(s):  
Speech Recognition ◽  
Graphical Models ◽  
Statistical Physics ◽  
Graphical Model ◽  
Markov Models ◽  
Hidden Markov ◽  
Probability Models ◽  
Model Framework ◽  
Estimation Algorithms ◽  
Special Cases

Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas, including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper presents a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.

scholarly journals Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study

2021 ◽  
Vol 11 (16) ◽  
pp. 7685
Author(s):  
Alexandre Martins ◽  
Inácio Fonseca ◽  
José Torres Farinha ◽  
João Reis ◽  
António J. Marques Cardoso
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Production Costs ◽  
Proper Function ◽  
Health State ◽  
Production Flow ◽  
Pulp Industry ◽  
Prediction Approach

The availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product’s quality. In this way, a method for predicting the equipment’s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment’s health state: “Proper Function”, “Alert state”, and “Equipment Failure”. The research described in this paper demonstrates how an equipment health diagnosis can be made using the HMM, through the collection of observations from various sensors, without information of machine failures occurrences. The approach developed demonstrated to be robust, even the complexity of the system, having the potential to be generalized to any other type of equipment.

COMBINING TEXT CLASSIFIERS AND HIDDEN MARKOV MODELS FOR INFORMATION EXTRACTION

2009 ◽  
Vol 18 (02) ◽  
pp. 311-329 ◽  
Author(s):  
FLÁVIA A. BARROS ◽  
EDUARDO F. A. SILVA ◽  
RICARDO B. C. PRUDÊNCIO ◽  
VALMIR M. FILHO ◽  
ANDRÉ C. A. NASCIMENTO
Keyword(s):  
Information Extraction ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Test Set ◽  
First Case ◽  
Machine Learning Approach ◽  
Text Classifiers ◽  
Precision Rate

In this paper, we propose a hybrid machine learning approach to Information Extraction by combining conventional text classification techniques and Hidden Markov Models (HMM). A text classifier generates a (locally optimal) initial output, which is refined by an HMM, providing a globally optimal classification. The proposed approach was evaluated in two case studies and the experiments revealed a consistent gain in performance through the use of the HMM. In the first case study, the implemented prototype was used to extract information from bibliographic references, reaching a precision rate of 87.48% in a test set with 3000 references. In the second case study, the prototype extracted information from author affiliations, reaching a precision rate of 90.27% in a test set with 300 affiliations.

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