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Activity Number:
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478
- Advanced Data Analysis with Bayesian Latent Variable Modeling
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #312378
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Title:
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Analysis of Vole Data via a Bayesian Mixture Model
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Author(s):
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Ori Rosen* and Noelle Samia and Osnat Stramer and Michael Bertolacci
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Companies:
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Univ of Texas at El Paso and Northwestern University and University of Iowa and University of Wollongong
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Keywords:
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Bayesian mixture;
Grey-sided vole;
Markov chain Monte Carlo
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Abstract:
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We develop a Bayesian mixture model for analyzing data on grey-sided voles derived from a monitoring program in Hokkaido, Japan. The number of voles caught at a given location and time is assumed to follow a binomial distribution that depends on the number of traps and probability of being trapped. This probability depends on a latent time series modeled as a mixture that accommodates covariates. Estimation and inference are performed using Markov chain Monte Carlo techniques.
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Authors who are presenting talks have a * after their name.
