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130 results for “Markov”
Age-by-maternal-age Markov chain with rewards analysis of variance in LRO in rotifers
<p>This set of data and code files is a supplement to the American Naturalist paper:<br> van Daalen et al. 2021 The contributions of maternal age heterogeneity to variance in lifetime reproductive output</p> <p>It provides the user the matrices (as first presented in Hernandez et al., 2020, PNAS) and the code to reproduce our analysis or apply the methods to their own data. Two multistate, age-by-maternal-age matrices with basic demographic information (survival and transitions, and fertility) are provided, as well as data to paramaterize a reward matrix. The code presents a Markov chain with rewards approach to calculating mean and variance in lifetime reproductive output from a multistate matrix model, and a method to decompose variance into contributions by individual heterogeneity (from maternal age) and stochasticity (due to inherent randomness in the outcomes of age-specific survival probabilities and age-specific reproductive output).</p>
A study on the Application of Markov Chains to Prioritize Test Cases
<p>The entire results and the inputs to run the experiment are available in this dataset. The data is composed of five folders one for each case study and one for the running example presented in Section 3. Within each folder, it is possible do see the input files, as well as the results, both for H1 and H2. The input formats can be consulted to understanding and potentially replication of the study. This dataset also contains the algorithm, in Python. The algorithm is available on GitHub, we did not put the link because of the blind review.</p>
Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - QEST 2024 Artefact
<p>This artifact accompanies the QEST+FORMATS 2024 paper "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes" (<a href="https://arxiv.org/abs/2406.08175">arXiv</a>). 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 results presented in Section 5 and Appendix D. Additionally, it also contains the original raw experimental data presented in Section 5 and Appendix D 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 1 and Example 2<br>- Table 1 in Section 5<br>- Table 3 in Appendix D<br>- Figure 5 in Appendix D<br>- Figure 6 in Appendix D<br>- Table 4 in Appendix D</p> <p><strong>Aritfact structure</strong><br>This artifact consists of the following files and folders:<br>- <code>data</code>: Contains the PRISM models, PRISM properties (queries) and original raw experimental data presented in Section 5 and Appendix D. Additionally, the log files and scripts for summarizing the raw experimental data are provided.<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>qest-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> <p><strong>Note on the versions:</strong> The first version (v1) contains the data and implementation at the point of the paper submission. The second version (v2) contains a Docker image and more detailed documentation and was evaluated by the QEST+FORMATS 2024 Artifact Evaluation Comittee and awarded the artifact evaluation badge. This version (v3) incorporates the feedback of the QEST+FORMATS 2024 artifact evaluation and contains improvements on the second version (v2).</p> <p><strong>Acknowledgments:</strong> We would like to thank the anonymous reviewers in the QEST+FORMATS Artifact Evaluation Committee for their valuable feedback. The authors were supported by the German Federal Ministry of Education and Research (BMBF) within the project SEMECO Q1 (03ZU1210AG) and by the German Research Foundation (DFG) through the Cluster of Excellence EXC 2050/1 (CeTI, project ID 390696704, as part of Germany’s Excellence Strategy) and the DFG Grant 389792660 as part of TRR 248 (Foundations of Perspicuous Software System).</p>
Datasets for optical tweezer autoregressive hidden Markov modeling
<p>Data to regenerate figures and tables analyzing optical tweezer data with arHMM. OT_arHMM.zip contains the ot_arhmm library needed to analyze these files. scripts.zip contains all the jupyter notebooks used to analyze data and make figures and tables. data.zip contains the raw data and processed_data.zip contains data that has already been put through the HMMs for analysis. These processed files can be generated from the raw data by running the analyze_all.ipynb notebook. All other notebooks require processed files to be present before running.</p>
Computing Expected Visiting Times and Stationary Distributions in Markov Chains: Fast and Accurate (Artifact)
<p>This artifact contains the raw data of our experiments as well as scripts and benchmarks to reproduce the experiments.<br>Furthermore, the considered version of [Storm](http://stormchecker.org) is included, which contains our implementation.</p> <p>Please also consider the artifact of the conference paper available at [zenodo](https://zenodo.org/records/10438916) which has been accepted by the TACAS Artifact evaluation committee.</p> <p><br>This artifact contains: <br>`LICENSE`: The license document.<br>`README.md`: The instructions.<br>`raw_data.zip`: The raw data obtained during our experiments<br>`raw_data_with_results.zip`: The raw data, also including the resulting stationary distributions and evts in an explicit format. (84 GB!)<br>`reproduce.zip` contains benchmarks and scripts for reproducing the experiments<br>`storm-0b1cae2a94f06984f3cf4cecf5a5090e9bc71a56.zip` is the exact Storm version we considered.</p>
Data from: Markov-modulated continuous-time Markov chains to identify site- and branch-specific evolutionary variation in BEAST
<p>Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behaviour. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference.</p>
Inferring Log-Based Behavioural System Models using Markov Chains
<p>The datasets we used for the bachelor thesis <em><strong>Inferring Log-Based Behavioural System Models using Markov Chains</strong></em>, consisting of log traces of the XRP Ledger Conensus Protocol split into 5 different datasets.</p>
Figure 3. Gaussian Markov random field skyride plots for 11 in Phylogeography of the Poecilimon luschani species group (Orthoptera, Tettigoniidae): a radiation strictly correlated with climatic transitions in the Pleistocene
Figure 3. Gaussian Markov random field skyride plots for 11 populations of the Poecilimon luschani species group. The median estimates are shown as a thick solid line, and the 95% highest posterior density limits are shown by the grey area. The background grey graphs show fluctuations in temperature during the respective periods of the Pleistocene. The upper and lower margins show the time in kiloyears (Kyr), the left margin shows the log of the effective population size (Nef), and the right margin the departure in temperature (ΔT in °C). Names of the glacial periods are indicated in the boxes at the bottom of the graphs. The background graph shows the temperature reconstruction of Vostok; modified from http://agwobserver.wordpress.com. A, İzmir (P. ledereri); B, Aydın (P. tuncayi); C, Kütahya (P. egrigozi); D, Balıkesir (P. helleri); E- Es¸en2 (P. l. chobanovi); F, Es¸en3 (P. l. chobanovi); G, Demre (P. l. luschani); H, Kalkan (P. l. luschani); I, Patara (P. l. luschani); J, Olympos (P. l. birandi); K, Bakırlıdag (P. l. birandi).
Parameter Synthesis for Markov Models: Prophesy and model files
<p>This artefact contains the Prophesy tool and all model files used for the evaluation in Sect. 11 of the paper "Parameter Synthesis for Markov Models". Further information can be found in the <em>ARTEFACT.md</em>.</p>
Affects Affect Affects: A Markov Chain
<p>Data for the article "<strong>Affects Affect Affects: A Markov Chain"</strong></p> <p>Data calculation for the probability vector in each step with the State transitions matrix, with initial state (S<sub>0</sub>) and the resulting calculation of the steady-state vector.</p>
Experiments for 'Efficient Sensitivity Analysis for Parametric Robust Markov Chains'
<p>This artifact accompanies the CAV 2023 paper with the title 'Efficient Sensitivity Analysis for Parametric Robust Markov Chains'. The artifact contains a docker file, which can be unzipped and then loaded with:</p> <pre><code>docker load -i prmc_sensitivity_cav23_docker.tar</code></pre> <p>Depending on your permissions, you may need to run this command with sudo. Please refer to the ReadMe for more information.</p> <p>The source code of the docker container is available on GitHub: <a href="https://github.com/LAVA-LAB/prmc-sensitivity">https://github.com/LAVA-LAB/prmc-sensitivity</a>.</p>
A Markov-switching spatiotemporal ARCH model (Datasets)
<p>The dataset used in the original article titled: A Markov-switching spatiotemporal ARCH model. It contains the prices of 26 Asian stock indices and 2 US stock indices spanning from 4 January 2011 to 30 December 2020.</p>
Data from: Markov-modulated Poisson processes as a new framework for analyzing capture-recapture data
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Data from: samc: An R package for connectivity modeling with spatial absorbing Markov chains
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Data from: Bayesian adaptive Markov Chain Monte Carlo estimation of genetic parameters
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Data from: Markov-modulated continuous-time Markov chains to identify site- and branch-specific evolutionary variation in BEAST
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Data from: A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies
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Data from: The behavior of Metropolis-coupled Markov chains when sampling rugged phylogenetic distributions
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Data from: Analysis of animal accelerometer data using hidden Markov models
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Data from: Markov switching autoregressive models for interpreting vertical movement data with application to an endangered marine apex predator
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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.