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1,066 results for “bayesian”

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zenodo36/100

Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data

Model, data, and a script to a paper of respective name

opencc-by-4.0May 2023View details →

zenodo36/100

Resources for "Disaggregating the carbon exchange of degrading permafrost peatlands using Bayesian deep learning"

This dataset contains all predictors, fluxes, and footprint weights used and described in our manuscript.

opencc-by-4.0May 2023View details →

zenodo36/100

YudengLin/memristorBDNN: Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning

This code repository is partly to support risk-sensitive reinforcement learning experiment in the manuscript "Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning" submitted to Nature Machine Intelligence.

openother-openMay 2023View details →

dryad36/100

Scalable Bayesian divergence time estimation with ratio transformations

<div class="page"> <div class="layoutArea"> <div class="column"> Divergence time estimation is crucial to provide temporal signals for dating biologically important events, from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly-correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original N - 1 internal node heights into a space of one height parameter and N - 2 ratio parameters. To make the analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in four pathogenic viruses (West Nile virus, rabies virus, Lassa virus and Ebola virus) and the coralline red algae. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples as well as for the algae example. Our method now also makes it computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study and reveals clearer multimodal distributions of the divergence times of some clades of interest. </div> </div> </div>

opencc-zeroJun 2023View details →

zenodo36/100

Redback: A Bayesian inference software package for electromagnetic transients - Example data

Data associated with plots in the publication Redback: A Bayesian inference software package for electromagnetic transients [<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>. 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>]

opencc-by-4.0Aug 2023View details →

zenodo36/100

Craton Radial Anisotropy and Low Velocity Zones imaged with Bayesian and LSQR methods

******************** README for Rad_anisotropy_BOYCE_GRL ***************************** This repository contains data files, inversion software, outputs and plotting codes to accompany the following submitted manuscript: Boyce, A., Bodin, T., Durand, S., Soergel, D., Debayle, E. Seismic Evidence for Craton Formation by Underplating and Development of the MLD (submitted) Geophysical Research Letters. The Rad_anisotropy_BOYCE_GRL_V2.tar repo contains:     • LSQR_inversion - Least Squares inversion of synthetic and real data sets, all input and results and plotting files are included.     • SRF_Forward_modelling - Codes to make Axisem synthetic models and synthetic data to be inverted with Bayesian Code. Also included are Greens functions from Axisem simulations at 5s minimum period and python codes for forward modelling synthetic S-to-p reciever functions from the Axisem outputs.     • Bayesian_inversion - Bayesian inversion of synthetic and real data sets, all input files, processed files and plotting codes necessary for reconstructing the posterior distributions are included. Please see https://github.com/alistairboyce11/RJ_MCMC for an up-to-date distribution.     • MLD_compilation - Our MLD compilation "MLD_compilation_BOYCE_2023.xlsx" and codes to combine this with Fu et al., (GRL 2022) and plot Figure 2 of main manuscript     • Plotting_for_manuscript         - Radial_anisotropy_models - codes used to extract data and make plots for Figures S1--S5. Tomographic models available to download at https://ds.iris.edu/ds/products/emc/         - Figures for Figures 1, 3, 4 in main manuscript.

opencc-by-4.0Aug 2023View details →

dryad36/100

Data from: Median-Joining Networks and Bayesian phylogenies often do not tell the same story

Inferring phylogenies among intraspecific individuals often yields unresolved relationships (i.e., polytomies). Consequently, methods that compute distance-based abstract networks, like Median-Joining Networks (MJNs), are thought to be more appropriate tools for reconstructing such relationships than traditional trees. Median-Joining Networks visualize all routes of relationships in the form of cycles, if needed, when traditional approaches cannot resolve them. However, the MJN method is a distance-based phenetic approach that does not involve character transformations and makes no reference to ancestor-descendant relationships. Although philosophical and theoretical arguments challenging the implication that MJNs reflect phylogenetic signal in the traditional sense have been presented elsewhere, an empirical comparison with a character-based approach is needed given the increasing popularity of MJN analysis in evolutionary biology. Here, we use the conservative Approximately Unbiased (AU) test to compare 85 cases of branching patterns of cycle-free MJNs and Bayesian Inference (BI) phylogenies using datasets from 55 empirical studies. By rooting the MJN analyses to provide directionality, we report substantial disagreement between computed MJNs and posterior distributions on BI phylogenies. The branching patterns in MJNs and BI phylogenies show significantly different relationships in 37.6% of cases. Among the relationships that do not significantly differ, 96.2% show alternative sets of relationships. Our results indicate that the two methods provide different measures of relatedness in a phylogenetic sense. Finally, our analyses also support previous observations of the statistical hypothesis testing by reconfirming the over-conservativeness of the Shimodaira-Hasegawa test versus the AU test.

opencc-zeroSep 2023View details →

ClinicalTrials.gov36/100

A Multicenter, Randomized, Double-blind, Placebo-controlled, Parallel-group, Bayesian Adaptive Randomization Design, Dose Response Study of the Efficacy of E2006 in Adults and Elderly Subjects With Ch

ClinicalTrials.gov study NCT01995838. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad36/100