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210 results for “Bayesian inference”
Inferring predator-prey interactions from camera traps: A Bayesian co-abundance modelling approach
<p><span>Predator-prey dynamics are a fundamental part of ecology, but directly studying interactions has proven difficult. The proliferation of camera trapping has enabled the collection of large datasets on wildlife, but researchers face hurdles inferring interactions from observational data. </span><span>Recent advances in </span><span>hierarchical c</span><span>o-abundance models infer species interactions while </span><span>accounting for two species' detection probabilities, shared responses to environmental covariates, and propagate uncertainty throughout the</span> <span>entire modelling process. However, current approaches remain </span><span>unsuitable for interacting species </span><span>whose natural densities differ by an order of magnitude and have contrasting detection probabilities, such as predator-prey interactions, which introduce zero-inflation and overdispersion in count histories. </span><span>Here we developed </span><span>a Bayesian hierarchical N-mixture co-abundance model that is </span><span>suitable for </span><span>inferring </span><span>predator-prey </span><span>interactions. We accounted for excessive zeros in count histories using an informed zero-inflated Poisson distribution in the abundance formula and accounted for overdispersion in count histories by including a random effect per sampling unit and sampling occasion in the detection probability formula. We demonstrate that models with these modifications outperform alternative approaches, improve model goodness-of-fit, and overcome parameter convergence failures. We highlight its utility using 20 camera trapping datasets </span><span>from 10 tropical forest landscapes in Southeast Asia and estimate four predator-prey relationships between tigers, clouded leopards, and muntjac and sambar deer. Tigers had a negative effect on muntjac abundance, providing support for top-down regulation, while clouded leopards had a positive effect on muntjac and sambar deer, likely driven by shared responses to unmodelled covariates like hunting. </span><span>This Bayesian co-abundance modelling approach to quantify predator-prey relationships </span><span>is widely applicable across species, ecosystems, and sampling approaches, and may be useful in forecasting cascading impacts following widespread predator declines. Taken together, this approach facilitates a nuanced and mechanistic understanding of food-web ecology.</span></p>
Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data
<p>Model, data, and a script to a paper of respective name</p>
Redback: A Bayesian inference software package for electromagnetic transients - Example data
<p>Data associated with plots in the publication <em><strong>Redback: A Bayesian inference software package for electromagnetic transients</strong></em> [<a href="https://arxiv.org/abs/2308.12806">arXiv:2308.12806</a>]. To reproduce the publication plots, you can use the notebook linked <a href="https://github.com/nikhil-sarin/redback/blob/master/examples/RedbackPaperPlots.ipynb">here</a>.</p> <p>Redback is an open-source, end-to-end Bayesian Inference software package for simulating and fitting electromagnetic transients. It provides an object-orientated Python interface to multiple sampling algorithms, models for several different electromagnetic transients, an interface to download and process the data for multiple transients from various catalogues, simulate transients for real surveys (e.g. ZTF, LSST) and fit the observations through Bayesian inference [<a href="https://github.com/nikhil-sarin/redback/tree/master">github.com/nikhil-sarin/redback</a>]</p> <p> </p>
Bayesian total-evidence inference resolves the position of the ant genus Phaulomyrma (Hymenoptera, Formicidae)
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zigzag: A Hierarchical Bayesian Mixture Model for Inferring the Expression State of Genes in Transcriptomes
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Data from: A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation
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Bayesian inference under the multispecies coalescent with ancient DNA sequences
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Dodonaphy - a Software using Hyperbolic Space for Bayesian Phylogenetic Inference
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Inferring predator-prey interactions from camera traps: A Bayesian co-abundance modelling approach
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Estimating epidemiological parameters of highly pathogenic avian influenza in common terns using exact Bayesian inference
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Fast Bayesian inference of phylogenies from multiple continuous characters
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Data and supplementary information from: Sequential bayesian phylogenetic inference
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Data for: Linking cell size, Vmax, and Km in phototrophs and chemotrophs: Insights from Bayesian inference
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Bayesian inference of ancestral host-parasite interactions under a phylogenetic model of host repertoire evolution
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FIGURE 10. Bayesian inference tree for 2,778 in A new species of the genus Pseudocrangonyx (Crustacea: Amphipoda Pseudocrangonyctidae) from Simbok Cave, Korea
FIGURE 10. Bayesian inference tree for 2,778 bp of nuclear 28S rRNA and histone H3, and mitochondrial COI and 16S rRNA markers. Numbers on nodes represent bootstrap values for maximum likelihood and Bayesian posterior probabilities. Taxonomically confused clades are highlighted in a gray box. The genus-group name Pseudocrangonyx is abbreviated to P.
FIGURE 1. Bayesian inference tree derived from the cyt b in Vanmanenia intermedia Fang, 1935, a valid hill-stream species of loach (Teleostei Gastromyzontidae) from the middle Yangtze River basin, Southwest China
FIGURE 1. Bayesian inference tree derived from the cyt b gene for nine species of Vanmanenia. Nodal numbers are posterior probability values larger than 50%.
Data for "Bayesian inference of mantle viscosity from whole-mantle density models"
<p><strong>Supplementary Data</strong><br> Rudolph, M.L., Moulik, P., and Lekic, V. (2020). Bayesian inference of mantle viscosity from whole-mantle density models. Geochemistry, Geophysics, Geosystems</p> <p>This data archive contains files needed to reproduce the figures from our 2020 G-Cubed paper, including the full ensemble solutions for mantle viscosity structure.</p>
Data from: Exact Bayesian inference for animal movement in continuous time
It is natural to regard most animal movement as a continuous-time process, generally observed at discrete times. Most existing statistical methods for movement data ignore this; the remainder mostly use discrete-time approximations, the statistical properties of which have not been widely studied, or are limited to special cases. We aim to facilitate wider use of continuous-time modelling for realistic problems. We develop novel methodology which allows exact Bayesian statistical analysis for a rich class of movement models with behavioural switching in continuous time, without any need for time discretization error. We represent the times of changes in behaviour as forming a thinned Poisson process, allowing exact simulation and Markov chain Monte Carlo inference. The methodology applies to data that are regular or irregular in time, with or without missing values. We apply these methods to GPS data from two animals, a fisher (Pekania [Martes] pennanti) and a wild boar (Sus scrofa), using models with both spatial and temporal heterogeneity. We are able to identify and describe differences in movement behaviour across habitats and over time. Our methods allow exact fitting of realistically complex movement models, incorporating environmental information. They also provide an essential point of reference for evaluating other existing and future approximate methods for continuous-time inference.
Data from: Using parsimony-guided tree proposals to accelerate convergence in Bayesian phylogenetic inference
<p>Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as `convergence') and in estimating the correct proportions of the different types of them (known as `mixing'). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical datasets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these datasets, ranging in size from 357 to 934 taxa and from 1,740 to 5,681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account.</p>
Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates
We propose a two-step procedure for estimating multiple migration rates in an approximate Bayesian computation (ABC) framework, accounting for global nuisance parameters. The approach is not limited to migration, but generally of interest for inference problems with multiple parameters and a modular structure (e.g. independent sets of demes or loci). We condition on a known, but complex demographic model of a spatially subdivided population, motivated by the reintroduction of Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters ancestral mutation rate and male mating skew have been estimated for the whole population in Aeschbacher et al. (Genetics 2012; 192: 1027). In the second step, we estimate in this study the migration rates independently for clusters of demes putatively connected by migration. For large clusters (many migration rates), ABC faces the problem of too many summary statistics. We therefore assess by simulation if estimation per pair of demes is a valid alternative. We find that the trade-off between reduced dimensionality for the pairwise estimation on the one hand and lower accuracy due to the assumption of pairwise independence on the other depends on the number of migration rates to be inferred: the accuracy of the pairwise approach increases with the number of parameters, relative to the joint estimation approach. To distinguish between low and zero migration, we perform ABC-type model comparison between a model with migration and one without. Applying the approach to microsatellite data from Alpine ibex, we find no evidence for substantial gene flow via migration, except for one pair of demes in one direction.
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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.