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Contents
Author ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿.¿.¿¿¿iv
1. Introduction to Bayesian Inferences for Infectious Diseases..................1
2. Bayesian Analysis ...........................................................................................5
3. Infectious Diseases .................................................................................. .....39
4. Bayesian Inference for Discrete Markov Chains:
Their Relevance to Infectious Diseases.....................................................59
5. Biological Examples Modeled by Discrete Markov Chains................ 113
6. Inferences for Markov Chains in Continuous Time.............................149
7. Bayesian Inference: Biological Processes that Follow a
Continuous Time Markov Chain...........................................................195
8. Additional Information about Infectious Diseases..............................253
Index ..................................................................................................... 315
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Contents
Author ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿.¿.¿¿¿iv
1. Introduction to Bayesian Inferences for Infectious Diseases..................1
2. Bayesian Analysis ...........................................................................................5
3. Infectious Diseases .................................................................................. .....39
4. Bayesian Inference for Discrete Markov Chains:
Their Relevance to Infectious Diseases.....................................................59
5. Biological Examples Modeled by Discrete Markov Chains................ 113
6. Inferences for Markov Chains in Continuous Time.............................149
7. Bayesian Inference: Biological Processes that Follow a
Continuous Time Markov Chain...........................................................195
8. Additional Information about Infectious Diseases..............................253
Index ..................................................................................................... 315
Show moreContents
Author ……………………………………………………………….….………iv
1. Introduction to Bayesian Inferences for Infectious Diseases..................1
2. Bayesian Analysis ...........................................................................................5
3. Infectious Diseases .................................................................................. .....39
4. Bayesian Inference for Discrete Markov Chains:
Their Relevance to Infectious Diseases.....................................................59
5. Biological Examples Modeled by Discrete Markov Chains................ 113
6. Inferences for Markov Chains in Continuous Time.............................149
7. Bayesian Inference: Biological Processes that Follow a
Continuous Time Markov Chain...........................................................195
8. Additional Information about Infectious Diseases..............................253
Index ..................................................................................................... 315
Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, the University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books are Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.
"This book will be useful for both masters and undergraduate
students in biostatistics, who are planning to pursue research in
Bayesian approaches towards epidemics applications"- Chitaranjan
Mahapatra, International Society for Clinical Biostatistics, 72,
2021
"Since most available textbooks for infectious disease modeling
present the subject from differential equations,
mathematical modeling perspective, this text is an important first
step towards filling the gap from the statistical
perspective."Marie V. Ozanne, Mount Holyoke College USA,
Biometrics: A Journal of the International Biometric Society,
December 2021.
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