A hierarchical Bayesian approach for parameter estimation in HIV models

H. T. Banks; Sarah Grove; Shuhua Hu; Yanyuan Ma

doi:10.1088/0266-5611/21/6/001

A hierarchical Bayesian approach for parameter estimation in HIV models

H. T. Banks, Sarah Grove, Shuhua Hu, Yanyuan Ma

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Abstract

A hierarchical Bayesian approach is developed to estimate parameters at both the individual and the population levels in a HIV model, with the implementation carried out by Markov Chain Monte Carlo (MCMC) techniques. Sample numerical simulations and statistical results are provided to demonstrate the feasibility of this approach.

Original language	English (US)
Pages (from-to)	1803-1822
Number of pages	20
Journal	Inverse Problems
Volume	21
Issue number	6
DOIs	https://doi.org/10.1088/0266-5611/21/6/001
State	Published - Dec 1 2005

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Signal Processing
Mathematical Physics
Computer Science Applications
Applied Mathematics

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10.1088/0266-5611/21/6/001

Cite this

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title = "A hierarchical Bayesian approach for parameter estimation in HIV models",

abstract = "A hierarchical Bayesian approach is developed to estimate parameters at both the individual and the population levels in a HIV model, with the implementation carried out by Markov Chain Monte Carlo (MCMC) techniques. Sample numerical simulations and statistical results are provided to demonstrate the feasibility of this approach.",

author = "Banks, {H. T.} and Sarah Grove and Shuhua Hu and Yanyuan Ma",

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AU - Hu, Shuhua

AU - Ma, Yanyuan

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N2 - A hierarchical Bayesian approach is developed to estimate parameters at both the individual and the population levels in a HIV model, with the implementation carried out by Markov Chain Monte Carlo (MCMC) techniques. Sample numerical simulations and statistical results are provided to demonstrate the feasibility of this approach.

AB - A hierarchical Bayesian approach is developed to estimate parameters at both the individual and the population levels in a HIV model, with the implementation carried out by Markov Chain Monte Carlo (MCMC) techniques. Sample numerical simulations and statistical results are provided to demonstrate the feasibility of this approach.

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EP - 1822

JO - Inverse Problems

JF - Inverse Problems

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A hierarchical Bayesian approach for parameter estimation in HIV models

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

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