scholarly journals A Test for the Underlying State-Structure of Hidden Markov Models: Partially Observed Capture-Recapture Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Anita Jeyam ◽  
Rachel S. McCrea ◽  
Roger Pradel
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Canada Geese ◽  
State Structure ◽  
Data Set ◽  
Partial Observations ◽  
Partially Observed ◽  
Capture Recapture ◽  
Special Case

Hidden Markov models (HMMs) are being widely used in the field of ecological modeling, however determining the number of underlying states in an HMM remains a challenge. Here we examine a special case of capture-recapture models for open populations, where some animals are observed but it is not possible to ascertain their state (partial observations), whilst the other animals' states are assigned without error (complete observations). We propose a mixture test of the underlying state structure generating the partial observations, which assesses whether they are compatible with the set of states observed in the complete observations. We demonstrate the good performance of the test using simulation and through application to a data set of Canada Geese.

scholarly journals Automatic detection of snow avalanches in continuous seismic data using hidden Markov models

2018 ◽  
Vol 18 (1) ◽  
pp. 383-396 ◽  
Author(s):  
Matthias Heck ◽  
Conny Hammer ◽  
Alec van Herwijnen ◽  
Jürg Schweizer ◽  
Donat Fäh
Keyword(s):  
Hidden Markov Models ◽  
Seismic Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Winter Season ◽  
Automatic Detection ◽  
Seismic Signals ◽  
Snow Avalanches ◽  
Data Set ◽  
Processing Steps

Abstract. Snow avalanches generate seismic signals as many other mass movements. Detection of avalanches by seismic monitoring is highly relevant to assess avalanche danger. In contrast to other seismic events, signals generated by avalanches do not have a characteristic first arrival nor is it possible to detect different wave phases. In addition, the moving source character of avalanches increases the intricacy of the signals. Although it is possible to visually detect seismic signals produced by avalanches, reliable automatic detection methods for all types of avalanches do not exist yet. We therefore evaluate whether hidden Markov models (HMMs) are suitable for the automatic detection of avalanches in continuous seismic data. We analyzed data recorded during the winter season 2010 by a seismic array deployed in an avalanche starting zone above Davos, Switzerland. We re-evaluated a reference catalogue containing 385 events by grouping the events in seven probability classes. Since most of the data consist of noise, we first applied a simple amplitude threshold to reduce the amount of data. As first classification results were unsatisfying, we analyzed the temporal behavior of the seismic signals for the whole data set and found that there is a high variability in the seismic signals. We therefore applied further post-processing steps to reduce the number of false alarms by defining a minimal duration for the detected event, implementing a voting-based approach and analyzing the coherence of the detected events. We obtained the best classification results for events detected by at least five sensors and with a minimal duration of 12 s. These processing steps allowed identifying two periods of high avalanche activity, suggesting that HMMs are suitable for the automatic detection of avalanches in seismic data. However, our results also showed that more sensitive sensors and more appropriate sensor locations are needed to improve the signal-to-noise ratio of the signals and therefore the classification.

scholarly journals Linkage Analysis and Haplotype Phasing in Experimental Autopolyploid Populations with High Ploidy Level Using Hidden Markov Models

2019 ◽  
Vol 9 (10) ◽  
pp. 3297-3314 ◽  
Author(s):  
Marcelo Mollinari ◽  
Antonio Augusto Franco Garcia
Keyword(s):  
Linkage Analysis ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Snp Markers ◽  
Genetic Linkage Analysis ◽  
Linkage Phase ◽  
Data Set ◽  
Ploidy Levels ◽  
Allele Dosage

Modern SNP genotyping technologies allow measurement of the relative abundance of different alleles for a given locus and consequently estimation of their allele dosage, opening a new road for genetic studies in autopolyploids. Despite advances in genetic linkage analysis in autotetraploids, there is a lack of statistical models to perform linkage analysis in organisms with higher ploidy levels. In this paper, we present a statistical method to estimate recombination fractions and infer linkage phases in full-sib populations of autopolyploid species with even ploidy levels for a set of SNP markers using hidden Markov models. Our method uses efficient two-point procedures to reduce the search space for the best linkage phase configuration and reestimate the final parameters by maximizing the likelihood of the Markov chain. To evaluate the method, and demonstrate its properties, we rely on simulations of autotetraploid, autohexaploid and autooctaploid populations and on a real tetraploid potato data set. The results show the reliability of our approach, including situations with complex linkage phase scenarios in hexaploid and octaploid populations.

scholarly journals Automatic detection of snow avalanches in continuous seismic data using hidden Markov models

2017 ◽  
Author(s):  
Matthias Heck ◽  
Conny Hammer ◽  
Alec van Herwijnen ◽  
Jürg Schweizer ◽  
Donat Fäh
Keyword(s):  
Hidden Markov Models ◽  
Seismic Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Automatic Detection ◽  
Seismic Signals ◽  
Snow Avalanches ◽  
Data Set ◽  
Temporal Behaviour ◽  
Processing Steps

Abstract. Snow avalanches generate seismic signals as many other mass movements. Detection of avalanches by seismic monitoring is highly relevant to assess avalanche danger. In contrast to other seismic events, signals generated by avalanches do not have a characteristic first arrival nor is it possible to detect different wave phases. In addition, the moving source character of avalanches increases the intricacy of the signals. Although it is possible to visually detect seismic signals produced by avalanches, reliable automatic detection methods for all types of avalanches do not exist yet. We therefore evaluate whether hidden Markov models (HMMs) are suitable for the automatic detection of avalanches in continuous seismic data. We analyzed data recorded during the winter season 2010 by a seismic array deployed in an avalanche starting zone above Davos, Switzerland. We first visually inspected the data and identified more than 200 events we assume to be generated by avalanches. Since most of the data consists of noise we first applied a simple amplitude threshold to reduce the amount of data. As first classification results were unsatisfying, we analyzed the temporal behaviour of the seismic signals for the whole data set and found that there is a high variability in the seismic signals. We therefore applied further post-processing steps to reduce the number of false alarms by defining a minimal duration for the detected event, implementing a voting based approach and analyzing the coherence of the detected events. We obtained the best classification results for events detected by at least 5 sensors and with a minimal duration of 12 s. These processing steps allowed identifying two known periods of high avalanche activity, suggesting that HMMs are suitable for the automatic detection of avalanches in seismic data. However our results also showed that more sensitive sensors and more appropriate sensor locations are needed to improve the signal-to-noise ratio of the signals and therefore the classification.

Variational Learning for Switching State-Space Models

2000 ◽  
Vol 12 (4) ◽  
pp. 831-864 ◽  
Author(s):  
Zoubin Ghahramani ◽  
Geoffrey E. Hinton
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
State Space ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
State Space Models ◽  
Data Set ◽  
Linear Dynamical Systems ◽  
Switching State

We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models—hidden Markov models and linear dynamical systems—and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs, Jordan, Nowlan, & Hinton, 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact expectation maximization (EM) algorithm cannot be applied. However, we present a variational approximation that maximizes a lower bound on the log-likelihood and makes use of both the forward and backward recursions for hidden Markov models and the Kalman filter recursions for linear dynamical systems. We tested the algorithm on artificial data sets and a natural data set of respiration force from a patient with sleep apnea. The results suggest that variational approximations are a viable method for inference and learning in switching state-space models.

scholarly journals Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence

2018 ◽  
Author(s):  
Manh Cuong Ngôe ◽  
Mads Peter Heide-Jørgensen ◽  
Susanne Ditlevsen
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Well Being ◽  
Diving Behaviour ◽  
The Arctic ◽  
Data Set ◽  
East Greenland ◽  
Species Response ◽  
Surface Time

AbstractDiving behaviour of narwhals is still largely unknown. We build three-state Hidden Markov models (HMM) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. In particular, diurnal patterns in diving behaviour for a marine mammal is being inferred, by using periodic B-splines with boundary knots in 0 and 24 hours. Several HMMs with covariates are used to characterize dive patterns. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. It is thus paramount to relax this strong and biologically unrealistic assumption to obtain trustworthy inferences.Author summaryNarwhals live in pristine environments. However, the increase in average temperatures in the Arctic and the concomitant loss of summer sea ice, as well as increased human activities, such as ship traffic and mineral exploration leading to increased noise pollution, are changing the environment, and therefore probably also the behavior and well-being of the narwhal. Here, we use probabilistic models to unravel the diving and feeding behavior of a male narwhal, tagged in East Greenland in 2013, and followed for nearly two months. The goal is to gain knowledge of the whales’ normal behavior, to be able to later detect possible changes in behavior due to climatic changes and human influences. We find that the narwhal uses around two thirds of its time searching for food, it typically feeds during deep dives (more than 350 m), and it can have extended periods, up to 3 days, without feeding activity.

scholarly journals Comparison of Two Objects Classification Techniques using Hidden Markov Models and Convolutional Neural Networks

2020 ◽  
Vol 19 (6) ◽  
pp. 1222-1254
Author(s):  
Carlos Sarmiento ◽  
Jesus Savage
Keyword(s):  
Neural Networks ◽  
Hidden Markov Models ◽  
Convolutional Neural Networks ◽  
Markov Models ◽  
Hidden Markov ◽  
Classification Systems ◽  
Battery Life ◽  
Data Set ◽  
Classification Of Images ◽  
Symbol Sequences

This paper presents a comparison between discrete Hidden Markov Models and Convolutional Neural Networks for the image classification task. By fragmenting an image into sections, it is feasible to obtain vectors that represent visual features locally, but if a spatial sequence is established in a fixed way, it is possible to represent an image as a sequence of vectors. Using clustering techniques, we obtain an alphabet from said vectors and then symbol sequences are constructed to obtain a statistical model that represents a class of images. Hidden Markov Models, combined with quantization methods, can treat noise and distortions in observations for computer vision problems such as the classification of images with lighting and perspective changes.We have tested architectures based on three, six and nine hidden states favoring the detection speed and low memory usage. Also, two types of ensemble models were tested. We evaluated the precision of the proposed methods using a public domain data set, obtaining competitive results with respect to fine-tuned Convolutional Neural Networks, but using significantly less computing resources. This is of interest in the development of mobile robots with computers with limited battery life, but requiring the ability to detect and add new objects to their classification systems.

scholarly journals Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models

2018 ◽  
Author(s):  
Marcelo Mollinari ◽  
Antonio Augusto Franco Garcia
Keyword(s):  
Linkage Analysis ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Snp Markers ◽  
Genetic Linkage Analysis ◽  
Linkage Phase ◽  
Data Set ◽  
Ploidy Levels ◽  
Allele Dosage

AbstractModern SNP genotyping technologies allow to measure the relative abundance of different alleles for a given locus and consequently to estimate their allele dosage, opening a new road for genetic studies in autopolyploids. Despite advances in genetic linkage analysis in autotetraploids, there is a lack of statistical models to perform linkage analysis in organisms with higher ploidy levels. In this paper, we present a statistical method to estimate recombination fractions and infer linkage phases in full-sib populations of autopolyploid species with even ploidy levels in a sequence of SNP markers using hidden Markov models. Our method uses efficient two-point procedures to reduce the search space for the best linkage phase configuration and reestimate the final parameters using the maximum-likelihood of the Markov chain. To evaluate the method, and demonstrate its properties, we rely on simulations of autotetraploid, autohexaploid and autooctaploid populations and on a real tetraploid potato data set. The results demonstrate the reliability of our approach, including situations with complex linkage phase scenarios in hexaploid and octaploid populations.Author summaryIn this paper, we present a complete multilocus solution based on hidden Markov models to estimate recombination fractions and infer the linkage phase configuration in full-sib mapping populations with even ploidy levels under random chromosome segregation. We also present an efficient pairwise loci analysis to be used in cases were the multilocus analysis becomes compute-intensive.

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
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