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Bayesian analysis
Interest
1
#703959
Immediately related elements
How this works
-
EIDM »
EIDM
EIDM ☜The Emerging Infectious Diseases Modelling Initiative (EIDM) – by the Public Health Agency of Canada and NSERC – aims to establish multi-disciplinary network(s) of specialists across the country in modelling infectious diseases to be applied to public needs associated with emerging infectious diseases and pandemics such as COVID-19. [1]☜F1CEB7
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Research interests »
Research interests
Research interests☜☜9FDEF6
■
Bayesian analysis
Bayesian analysis☜☜9FDEF6
►
Alexandra Schmidt »
Alexandra Schmidt
Alexandra Schmidt☜Alexandra M. Schmidt is Professor of Biostatistics and holds the endowed University Chair in the Department of Epidemiology, Biostatistics and Occupational Health (EBOH) at McGill University. Currently, she is the Program Director of the Biostatistics Graduate Program.☜FFFACD
►
David A. Stephens »
David A. Stephens
David A. Stephens☜Professor in the Department of Mathematics and Statistics and Vice-Dean in the Faculty of Science at McGill University.☜FFFACD
►
Michael Y Li »
Michael Y Li
Michael Y Li☜Professor of Mathematics in the Department of Mathematical and Statistical Sciences at the University of Alberta, and Director of the Information Research Lab (IRL).☜FFFACD
►
Rob Deardon »
Rob Deardon
Rob Deardon☜Associate Professor in the Department of Production Animal Health in the Faculty of Veterinary Medicine and the Department of Mathematics and Statistics in the Faculty of Science at the University of Calgary.☜FFFACD
►
Derek Bingham »
Derek Bingham
Derek Bingham☜Professor of Statistics and Actuarial Science, Simon Fraser University.☜FFFACD
►
Hanna Jankowski »
Hanna Jankowski
Hanna Jankowski☜Professor in the Department of Mathematics and Statistics at York University.☜FFFACD
►
Juxin Liu »
Juxin Liu
Juxin Liu☜Professor of Statistics in the Department of Mathematics and Statistics at the University of Saskatchewan☜FFFACD
►
Mathieu Maheu-Giroux »
Mathieu Maheu-Giroux
Mathieu Maheu-Giroux☜Canada Research Chair (Tier 2) in Population Health Modeling and Associate Professor, McGill University.☜FFFACD
►
Paul Gustafson »
Paul Gustafson
Paul Gustafson☜Professor and Head of the Department of Statistics at the University of British Columbia Vancouver.☜FFFACD
►
COVID Prevalence Estimate »
COVID Prevalence Estimate
COVID Prevalence Estimate☜An implementation of Bayesian inference and prediction of COVID-19 point-prevalence.☜FFFACD
►
SimBIID »
SimBIID
SimBIID☜Simulation-Based Inference Methods for Infectious Disease Model – Provides some code to run simulations of state-space models, and then use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) algorithm of Toni et al. (2009) and a bootstrap particle filter based particle Markov chain Monte Carlo (PMCMC) algorithm (Andrieu et al., 2010). Also provides functions to plot and summarise the outputs. [1]☜FFFACD
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Dempster-Shafer Bayesian Network inference package »
Dempster-Shafer Bayesian Network inference package
Dempster-Shafer Bayesian Network inference package☜DS-BN is a C++ executable that accepts input data related to probabilistic belief networks and Dempster-Shafer belief networks through the use of files. According to provided instructions from one of the files, performs computations and writes inferred probability distributions or Dempster-Shafer models into an output file. [2]☜FFFACD
►
Lloyd T. Elliott »
Lloyd T. Elliott
Lloyd T. Elliott☜Assistant Professor, Statistics and Actuarial Science at Simon Fraser University.☜FFFACD
►
Edward Thommes »
Edward Thommes
Edward Thommes ☜Edward W. Thommes is an Adjunct Professor of Mathematics at the University of Guelph and at York University. He is a Global Modeling Lead in the Modeling, Epidemiology and Data Science (MEDS) team of Sanofi Vaccines, an Affiliate Researcher in the Waterloo Institute for Complexity and Innovation (WICI), and a member of the Strategic Advisory Committee for the Mathematics for Public Health program at the Fields Institute.☜FFFACD
◄
Markov chain »
Markov chain
Markov chain☜Markov chain Monte Carlo methods (McMC)☜FFFACD
◄
Probabilistic inference models »
Probabilistic inference models
Probabilistic inference models☜Probabilistic inference models to address bias, validity and credibility of infectious disease data. This project addresses data management and usage for decision support related to EIDM.☜FFFACD
◄
Bayesian Evolutionary Analysis by Sampling Trees (BEAST) »
Bayesian Evolutionary Analysis by Sampling Trees (BEAST)
Bayesian Evolutionary Analysis by Sampling Trees (BEAST)☜BEAST 2 is an open source cross-platform software package for analysing genetic sequences in a Bayesian phylogenetic framework. BEAST 2 provides a growing collection of new models tailored specifically to particular data sets and/or research questions.☜FFFACD
◄
23/08/14 Taming the BEAST workshop »
23/08/14 Taming the BEAST workshop
23/08/14 Taming the BEAST workshop☜Bayesian Evolutionary Analysis by Sampling Trees: Taming the BEAST – August 14 to 18, 2023, Howe Sound Inn & Brewing, Squamish, British Columbia. BEAST 2 is an open source cross-platform software package for analysing genetic sequences in a Bayesian phylogenetic framework. Participants will be equipped with the skills and core knowledge to confidently perform and interpret inference generated from phylogenetic and phylodynamic analyses.☜FFFACD
◄
2022/03/03 Technique in Biostatistics and Epidemiology »
2022/03/03 Technique in Biostatistics and Epidemiology
2022/03/03 Technique in Biostatistics and Epidemiology☜Module 1: Applied probabilistic programming – Probabilistic programming provides a flexible and automatic implementation of efficient procedures for Bayesian statistical analysis. This module will provide a general background on the Hamiltonian Monte Carlo sampler, which underlies the popular Stan programming language, followed by a practical demonstration of Stan in R.☜FFFACD
◄
23/11/16 Rob Deardon »
23/11/16 Rob Deardon
23/11/16 Rob Deardon☜Bayesian behavioural change epidemic models.☜FFFACD
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Redagavo:
David Price
NodeID:
#703959
Node type:
Interest
Įvedimo data (GMT):
11/5/2022 4:05:00 PM
Paskutinės redakcijos data (GMT laikas):
3/9/2023 9:24:00 AM
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