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10 results for “Bayesian statistics”
Supplementary Data to *Informative and adaptive distances and summary statistics in approximate Bayesian computation*
<p>Supplementary code and data to <strong>Informative and adaptive distances and summary statistics in approximate Bayesian computation</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_slad, of which this archive is a snapshot.</p>
GCTB SBayesR shrunk sparse linkage disequilibrium matrices for HM3 variants, summary statistics and predictors generated from "Improved polygenic prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al. 2019.
<p>GCTB LD matrices and results for HapMap 3 variants and 2.8M variants, which were used for</p> <p>simulation, cross-validation and across biobank analyses in the manuscript "Improved polygenic</p> <p>prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al.</p> <p>2019.</p> <p>Unzip and see README for further details.</p>
Experimental and synthetic datasets supporting FITSA: Statistical analysis of fluorescence intensity transients with Bayesian methods
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Missing data in sea turtle population monitoring: a Bayesian statistical framework accounting for incomplete sampling
<p>Monitoring how populations respond to sustained conservation measures is essential to detect changes in their population status and determine the effectiveness of any interventions. In the case of sea turtles, their populations are difficult to assess because of their complicated life histories. Ground-derived clutch counts are most often used as an index of population size for sea turtles; however, data are often incomplete with varying sampling intensity within and among sites and seasons. To address these issues, we: (1) develop a Bayesian statistical modelling framework that can be used to account for sampling uncertainties in a robust probabilistic manner within a given site and season; and (2) apply this to a previously unpublished long-term sea turtle dataset (n = 17 years) collated for the Republic of the Congo, which hosts two sympatrically nesting species of sea turtle (leatherback turtle [<em>Dermochelys coriacea</em>] and olive ridley turtle [<em>Lepidochelys olivacea</em>]). The results of this analysis suggest that leatherback turtle nesting levels dropped initially and then settled into quasi-cyclical levels of interannual variability, with an average of 573 (mean, 95% prediction interval: 554–626) clutches laid annually between 2012 and 2017. In contrast, nesting abundance for olive ridley turtles has increased more recently, with an average of 1,087 (mean, 95% prediction interval: 1,057–1,153) clutches laid annually between 2012 and 2017. These findings highlight the regional and global importance of this rookery with the Republic of the Congo, hosting the second largest documented populations of olive ridley and the third largest for leatherback turtles in Central Africa; and the fourth largest non-arribada olive ridley rookery globally. Furthermore, whilst the results show that Congo's single marine and coastal national park provides protection for over half of sea turtle clutches laid in the country, there is scope for further protection along the coast. Although large parts of the African coastline remain to be adequately monitored, the modelling approach used here will be invaluable to inform future status assessments for sea turtles given that most datasets are temporally and spatially fragmented. </p>
Missing data in sea turtle population monitoring: a Bayesian statistical framework accounting for incomplete sampling
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Bayesian inference of tree species using diffusion models: tabulated posterior statistics for SNAPP and SNAPPER analyses
<p>We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of datasets containing hundreds or thousands of individuals. The method, which we call \snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements and accuracy recovering known model parameters. A re-analysis of soybean SNP data demonstrates that the models implemented in \snapp and \snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP dataset sampled from 399 fresh water turtles in 41 populations.</p>
Data from: Predictive Bayesian selection of multistep Markov chains, applied to the detection of the hot hand and other statistical dependencies in free throws
When extended to data from the 2016--2017 NBA season specifically for LeBron James, a model depending on the previous shot (single-step Markovian) does not clearly beat a model with independent outcomes. An error-correcting variable length model of two parameters, where James shoots a higher percentage after a missed free throw than otherwise, is more predictive than either model.
Bayesian Statistics and Markov Chain Monte Carlo
<p>Recording of the presentation given at the Summer School</p>
Data from: Predictive Bayesian selection of multistep Markov chains, applied to the detection of the hot hand and other statistical dependencies in free throws
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Bayesian inference of tree species using diffusion models: tabulated posterior statistics for SNAPP and SNAPPER analyses
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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.