Stochastic models with applications to genetics, cancers, AIDS, and other biomedical systems / by Wai-Yuan Tan.

Format:

Book

Author/Creator:

Tan, W. Y., 1934- author.

Series:

Series on concrete and applicable mathematics ; volume 19.

Series on concrete and applicable mathematics ; volume 19

Language:

English

Subjects (All):

Medicine--Mathematical models.

Medicine.

Stochastic processes.

Genetics--Mathematical models.

Genetics.

AIDS (Disease)--Mathematical models.

AIDS (Disease).

Cancer--Mathematical models.

Cancer.

Physical Description:

1 online resource (523 p.)

Edition:

Second edition.

Place of Publication:

New Jersey : World Scientific, [2016]

Language Note:

English

Summary:

"This book presents a systematic treatment of Markov chains, diffusion processes and state space models, as well as alternative approaches to Markov chains through stochastic difference equations and stochastic differential equations. It illustrates how these processes and approaches are applied to many problems in genetics, carcinogenesis, AIDS epidemiology and other biomedical systems. One feature of the book is that it describes the basic MCMC (Markov chain and Monte Carlo) procedures and illustrates how to use the Gibbs sampling method and the multilevel Gibbs sampling method to solve many problems in genetics, carcinogenesis, AIDS and other biomedical systems. As another feature, the book develops many state space models for many genetic problems, carcinogenesis, AIDS epidemiology and HIV pathogenesis. It shows in detail how to use the multilevel Gibbs sampling method to estimate (or predict) simultaneously the state variables and the unknown parameters in cancer chemotherapy, carcinogenesis, AIDS epidemiology and HIV pathogenesis. As a matter of fact, this book is the first to develop several state space models for many genetic problems, carcinogenesis and other biomedical problems. To emphasize special applications to medical problems, in this new edition the book has added a new chapter to illustrate how to develop biologically-supported stochastic models and state space models of carcinogenesis in human beings. Specific examples include hidden Markov models and state space models for human colon cancer, human liver cancer and some human pediatric cancers such as retinoblastoma and hepatoblastoma. The book also gives examples to illustrate how to develop procedures to assess cancer risk of environmental agents through initiation-promotion protocols."-- Provided by publisher.

Contents:

Contents; Preface; 1 Introduction; 1.1. Some Basic Concepts of Stochastic Processes and Examples; 1.2. Markovian and Non-Markovian Processes, Markov Chains and Examples; 1.3. Diffusion Processes and Examples; 1.4. State Space Models and Hidden Markov Models; 1.5. The Scope of the Book; 1.6. Complements and Exercises; References; 2 Discrete Time Markov Chain Models in Genetics and Biomedical Systems; 2.1. Examples fromGenetics and AIDS; 2.2. The Transition Probabilities and Computation; 2.3. The Structure and Decomposition of Markov Chains

2.4. Classification of States and the Dynamic Behavior of Markov Chains2.5. The Absorption Probabilities of Transient States; 2.5.1. The case when CT is finite; 2.5.2. The case when CT is infinite; 2.6. The Moments of First Absorption Times; 2.6.1. The case when CT is finite; 2.7. Some Illustrative Examples; 2.8. Finite Markov Chains; 2.8.1. The canonical form of transition matrix; 2.8.2. Absorption probabilities of transient states in finite Markov chains; 2.9. Stochastic Difference Equation for Markov Chains With Discrete Time; 2.9.1. Stochastic difference equations for finite Markov chains

2.9.2. Markov chains in the HIV epidemic in homosexual or IV drug user populations2.10.Complements and Exercises; 2.11.Appendix; 2.11.1. The Hardy-Weinberg law in population genetics; 2.11.1.1. The Hardy-Weinberg law for a single locus in diploid populations; 2.11.1.2. The Hardy-Weinberg law for linked loci in diploid populations; 2.11.2. The inbreeding mating systems; 2.11.3. Some mathematical methods for computing An, the nth power of a square matrix A; References; 3 Stationary Distributions and MCMC in Discrete Time Markov Chains; 3.1. Introduction

3.2. The Ergodic States and Some Limiting Theorems3.3. Stationary Distributions and Some Examples; 3.4. Applications of Stationary Distributions and Some MCMC Methods; 3.4.1. The Gibbs sampling method; 3.4.2. The weighted bootstrap method for generating random samples; 3.4.3. The Metropolis-Hastings algorithm; 3.5. Some Illustrative Examples; 3.6. Estimation of Linkage Fraction by Gibbs Sampling Method; 3.7. Complements and Exercises; 3.8. Appendix: A Lemma for Finite Markov Chains; References; 4 Continuous-Time Markov Chain Models in Genetics, Cancers and AIDS; 4.1. Introduction

4.2. The Infinitesimal Generators and an Embedded Markov Chain4.3. The Transition Probabilities and Kolmogorov Equations; 4.4. Kolmogorov Equations for Finite Markov Chains with Continuous Time; 4.5. Complements and Exercises; References; 5 Absorption Probabilities and Stationary Distributions in Continuous-Time Markov Chain Models; 5.1. Absorption Probabilities and Moments of First Absorption Times of Transient States; 5.1.1. The case when CT is finite; 5.2. The Stationary Distributions and Examples; 5.3. Finite Markov Chains and the HIV Incubation Distribution

5.3.1. Some general results in finite Markov chains with continuous Time

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

981-4390-95-X