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130 results for “Markov”
Profile Comparer Extended: phylogeny of LPMO families using profile hidden Markov model alignments
<p>searchable pdf phylogenetic tree (Fig S1) and sequence data (Table S1) belonging to the paper "Profile Comparer Extended: phylogeny of LPMO families using profile hidden Markov model alignments"</p>
Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
The evolutionary dynamics of complex ecological traits – including multistate representations of diet, habitat, and behavior – remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.
Author Name Disambiguation using Markov Chain Monte Carlo (MCMC)
<p>This repository contains the files processed from the <a href="https://zenodo.org/record/5675801#.Y2APcOzMJhE">Aminer-534K</a> Knowledge graph for the master's thesis on Author Name Disambiguation.</p>
Prioritizing test cases with Markov Chains: a Preliminary Investigation
<p>This replication package includes the inputs to replicate the results shown in a paper under revision. Data is structured in four folders, one for each considered case study (there is an initial example besides the three case studies reported in Section 5). Within each folder, it is possible to inspect the input files as well as the results, for both proposed heuristics, i.e., H1 and H2. The input formats can be consulted to understanding and replicating the study. The dataset also contains the algorithm written in Python.</p>
Hidden Markov models with serial correlation for identifying stock-recruitment regime shifts
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From pup to predator; generalized hidden Markov models reveal rapid development of movement strategies in a naïve long‐lived vertebrate
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Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
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Data from: Hidden Markov models reveal tactical adjustment of temporally-clustered courtship displays in response to the behaviors of a robotic female
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Markov Automata with Multiple Objectives: Supplemental Material
<p>This data set contains benchmark files for multi-objective model checking of Markov Automata with <a href="https://www.stormchecker.org/">Storm</a>.</p>
Data from: A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies
An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor–response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their ability to incorporate latent variables and model direct and indirect links between state variables and capture probabilities.
Data from: Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models
1. Hidden Markov models are prevalent in animal movement modelling, where they are widely used to infer behavioural modes and their drivers from various types of telemetry data. To allow for meaningful inference, observations need to be equally spaced in time, or otherwise regularly sampled, where the corresponding temporal resolution strongly affects what kind of behaviours can be inferred from the data. 2. Recent advances in biologging technology have led to a variety of novel telemetry sensors which often collect data from the same individual simultaneously at different time scales, e.g. step lengths obtained from GPS tags every hour, dive depths obtained from time-depth recorders once per dive, or accelerations obtained from accelerometers several times per second. However, to date, statistical machinery to address the corresponding complex multi-stream and multi-scale data is lacking. 3. We propose hierarchical hidden Markov models as a versatile statistical framework that naturally accounts for differing temporal resolutions across multiple variables. In these models, the observations are regarded as stemming from multiple, connected behavioural processes, each of which operates at the time scale at which the corresponding variables were observed. 4. By jointly modelling multiple data streams, collected at different temporal resolutions, corresponding models can be used to infer behavioural modes at multiple time scales, and in particular help to draw a much more comprehensive picture of an animal's movement patterns, e.g. with regard to long-term vs. short-term movement strategies. 5. The suggested approach is illustrated in two real-data applications, where we jointly model i) coarse-scale horizontal and fine-scale vertical Atlantic cod (Gadus morhua) movements throughout the English Channel, and ii) coarse-scale horizontal movements and corresponding fine-scale accelerations of a horn shark (Heterodontus francisci) tagged off the Californian coast.
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals).
Data from: Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits
Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes – hierarchical and hidden – which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of structured Markov models with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of gene regulatory networks and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing "tail color problem," in which the trait is scored for species with tails of different colors or no tails. It also presents a previously unknown issue called the "two-scientist paradox," in which the nature of coding the trait and the hidden processes driving the trait's evolution are confounded; failing to account for the hidden process may result in a bias, which can be avoided by using hidden state models. All this provides a clear guideline for coding traits into characters. This paper gives practical examples of using the new framework for phylogenetic inference and comparative analysis.
Data from: Markov switching autoregressive models for interpreting vertical movement data with application to an endangered marine apex predator
1.Time series of animal movement obtained from bio-loggers are becoming widely used across all taxa. These data are nowadays of high quality, combining high resolution with precision, as the tags are able to collect for longer times and store larger quantities of data. Due to their nature, high-frequency data sequences often pose non-trivial problems in time series analysis: non-linearity, non-Normality, non-stationarity, and long memory. These issues can be tackled by modelling the data sequence as a realization of a stochastic regime switching process. 2. We suggest a novel Markov switching autoregressive model where the hidden Markov chain is non-homogeneous, with time-varying transition probabilities, whose dynamics depend on the dynamics of some contemporary categorical covariates. 3. To illustrate the use of the method, we apply it to the depth profiles of four individuals of flapper skate (Dipturus cf. intermedia) in order to identify swimming behaviours. Individual time series were obtained from data storage tags that recorded pressure every two minutes. The environmental covariates used were lunar phase (a proxy for the spring-neap tidal cycle), lunar cycle, and diel cycle. For all individuals two states (or regimes) were always selected (the autoregressive order was either three or four), representing different regimes of animal activity, i.e., state 1 for resting or horizontal swimming or slow vertical movement; state 2 for fast ascending and descending. The cycle of the four lunar phases was the only environmental covariate that explained the hidden state dynamics in all individuals, whereas lunar cycle was selected for two individuals and diel cycle for one only. 4. The method is an efficient approach to fit one-dimensional tag data using categorical environmental covariates, and to classify the observations into a small number of states representing individual behaviours of tagged individuals.
Data from: Markov-modulated Poisson processes as a new framework for analyzing capture-recapture data
1.Opportunistic capture-recapture data consists of observations over non-constant time-intervals and so fails to satisfy the basic assumptions of traditional capture-recapture models. Analyzing opportunistic capture-recapture data is often done by discretizing time-intervals or summarizing data, but without taking into account the continuous-time process of the state and/or the capture. 2.To deal with non-constant time-intervals, continuous-time closed capture-recapture models have been proposed by Yip et al. (1996), Hwang & Chao (2002), Schofield et al. (2017) for estimating population size. More recently, a continuous-time Cormack-Jolly-Seber model has been proposed by Fouchet et al. (2016) to reduce bias in survival rates, and a two-state process has been proposed by Choquet et al. (2017) to estimate reproduction rates and survival rates of young within a season. 3.The aim of the current study is to demonstrate how an approach based on a Markov-modulated Poisson process (MMPP) (Freed & Shepp, 1982) allows, in a similar way to a multistate model, to model opportunistic data using several states. To this end, several multistate models were rewritten as MMPP models, showing, the potential for this approach to address the ecological questions as multistate models, but using an extended data framework. In particular, it is a useful framework for dealing with data that has unordered levels of uncertainty. 4.The methods were illustrated using simulations and analysis of data on the Alpine ibex (Capraibex).
Data from: Analysis of animal accelerometer data using hidden Markov models
Use of accelerometers is now widespread within animal biologging as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data, there is a natural dependence between observations of behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (HMMs). Depending on the aim of an analysis, an HMM can be used for state prediction or to make inferences about drivers of behaviour. For state prediction, a supervised learning approach can be applied. That is, an HMM is trained to classify unlabelled acceleration data into a finite set of pre-specified categories. An unsupervised learning approach can be used to infer new aspects of animal behaviour when biologically meaningful response variables are used, with the caveat that the states may not map to specific behaviours. We provide the details necessary to implement and assess an HMM in both the supervised and unsupervised learning context and discuss the data requirements of each case. We outline two applications to marine and aerial systems (shark and eagle) taking the unsupervised learning approach, which is more readily applicable to animal activity measured in the field. HMMs were used to infer the effects of temporal, atmospheric and tidal inputs on animal behaviour. Animal accelerometer data allow ecologists to identify important correlates and drivers of animal activity (and hence behaviour). The HMM framework is well suited to deal with the main features commonly observed in accelerometer data and can easily be extended to suit a wide range of types of animal activity data. The ability to combine direct observations of animal activity with statistical models, which account for the features of accelerometer data, offers a new way to quantify animal behaviour and energetic expenditure and to deepen our insights into individual behaviour as a constituent of populations and ecosystems.
Dataset from "Employing hidden Markov models to assess the genetic content of genome assemblies"
<p>Dataset used to reach the conclusions in "Employing hidden Markov models to assess the genetic content of genome assemblies".</p>
Accompanying simulated data for "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity"
<p>The multilevel hidden Markov model (MHMM) is a promising vehicle to investigate latent dynamics over time in social and behavioral processes. By including continuous individual random effects, the model accommodates variability between individuals, providing individual-specific trajectories and facilitating the study of individual differences. However, the performance of the MHMM has not been sufficiently explored. Currently, there are no practical guidelines on the sample size needed to obtain reliable estimates related to categorical data characteristics We performed an extensive simulation to assess the effect of the number of dependent variables (1-4), the number of individuals (5-90), and the number of observations per individual (100-1600) on the estimation performance of group-level parameters and between-individual variability on a Bayesian MHMM with categorical data of various levels of complexity. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. Meanwhile, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the complexity of the data and the study objectives of the practitioners.</p> <p>This repository contains data generated for the manuscript: "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity". It comprehends: (1) model outputs (maximum a posteriori estimates) for each repetition (n=100) of each scenario (n=324) of the main simulation, (2) complete model outputs (including estimates for 4000 MCMC iterations) for two chains of each repetition (n=3) of each scenario (n=324). Please note that the empirical data used in the manuscript is not available as part of this repository. A subsample of the data used in the empirical example are openly available as an example data set in the R package <a href="https://cran.r-project.org/web/packages/mHMMbayes/index.html">mHMMbayes on CRAN</a>. The full data set is available on request from the authors.</p>
Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact - PEVA
<p><strong>Summary</strong><br>This artifact accompanies the PEVA submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes". It contains the implementation (<code>switss-multi</code>) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the experimental results presented in Section 6. Additionally, it also contains the original raw experimental data presented in Section 6 and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation <code>switss-multi</code> and describe how to use our tool via its command-line and programmatically via its Python interface.</p> <p><strong>Relation to paper</strong><br>This artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:<br>- The toy examples presented in Example 12, Example 14, Example 22 and Example 34<br>- Table 3 in Section 6<br>- Table 4 in Section 6<br>- Table 5 in Section 6<br>- Table 8 in Section 6<br>- Figure 9 in Section 6<br>- Figure 10 in Section 6<br>- Figure 11 in Section 6</p> <p><strong>Structure</strong><br>This artifact consists of the following files and folders:<br>- <code>data</code>: Contains original raw experimental data presented in Section 6. Additionally, the log files and scripts for summarizing the raw experimental data are provided.<br>- <code>switss-multi/experiments</code>: Contains the PRISM models, PRISM properties (queries) and scripts for running the experiments.<br>- <code>switss-multi</code>: The source code of the implementation of our presented techniques.<br>- <code>switss-multi-docs</code>: A documentation of the Python API of <code>switss-multi</code>.<br>- <code>peva-docker-image.tar.gz</code>: The compressed Docker image, with the installed implementation (<code>switss-multi</code>), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the <code>data</code> folder, in case you want to run the analysis scripts on the original data.<br>- <code>docker-results</code>: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.<br>- <code>LICENSE</code>: The license of this artifact (MIT license).<br>- <code>GUROBI-EULA</code>: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).<br>- <code>GPL-3.0</code>: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.</p>
Data extraction form - A Systematic Literature Review on Prioritizing Software Test Cases using Markov Chains
<p>A data extraction form was created to gather all relevant data from the identified studies and manage the selection process in this systematic literature review. Some of the main information presented in this form was followed by a protocol, identifier (id) for each returned study, bibliographic reference, and answers to research questions. This catalog helps us in the data extraction and synthesis procedures and may be used by potentially interested, for example, for updating or replication. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.