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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Research output: Contribution to journal › Article › peer-review

17 Scopus citations

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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

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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.",

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AU - Grove, Sarah

AU - Hu, Shuhua

AU - Ma, Yanyuan

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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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UR - https://www.scopus.com/pages/publications/28544439751#tab=citedBy

U2 - 10.1088/0266-5611/21/6/001

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JO - Inverse Problems

JF - Inverse Problems

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ER -

A hierarchical Bayesian approach for parameter estimation in HIV models

Abstract

UN SDGs

All Science Journal Classification (ASJC) codes

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