The Resource Stochastic models with applications to genetics, cancers, AIDS, and other biomedical systems, by WaiYuan Tan, (electronic resource/)
Stochastic models with applications to genetics, cancers, AIDS, and other biomedical systems, by WaiYuan Tan, (electronic resource/)
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The item Stochastic models with applications to genetics, cancers, AIDS, and other biomedical systems, by WaiYuan Tan, (electronic resource/) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Old Colony Library Network.This item is available to borrow from 2 library branches.
Resource Information
The item Stochastic models with applications to genetics, cancers, AIDS, and other biomedical systems, by WaiYuan Tan, (electronic resource/) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Old Colony Library Network.
This item is available to borrow from 2 library branches.
 Language
 eng
 Edition
 2nd edition.
 Extent
 1 online resource (access may be restricted)
 Contents

 Preface; 1 Introduction; 1.1. Some Basic Concepts of Stochastic Processes and Examples; 1.2. Markovian and NonMarkovian 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 HardyWeinberg law in population genetics; 2.11.1.1. The HardyWeinberg law for a single locus in diploid populations; 2.11.1.2. The HardyWeinberg 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 MetropolisHastings 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 ContinuousTime 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 ContinuousTime 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
 Label
 Stochastic models with applications to genetics, cancers, AIDS, and other biomedical systems
 Title
 Stochastic models with applications to genetics, cancers, AIDS, and other biomedical systems
 Statement of responsibility
 by WaiYuan Tan
 Language
 eng
 Cataloging source
 N$T
 Dewey number
 610.1/5195
 Index
 index present
 LC call number
 R853.M3
 LC item number
 T36 2015eb
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 Series statement
 Series on concrete and applicable mathematics
 Series volume
 volume 19
 Label
 Stochastic models with applications to genetics, cancers, AIDS, and other biomedical systems, by WaiYuan Tan, (electronic resource/)
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents

 Preface; 1 Introduction; 1.1. Some Basic Concepts of Stochastic Processes and Examples; 1.2. Markovian and NonMarkovian 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 HardyWeinberg law in population genetics; 2.11.1.1. The HardyWeinberg law for a single locus in diploid populations; 2.11.1.2. The HardyWeinberg 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 MetropolisHastings 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 ContinuousTime 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 ContinuousTime 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
 Control code
 ocn928387326
 Dimensions
 unknown
 Edition
 2nd edition.
 Extent
 1 online resource (access may be restricted)
 File format
 unknown
 Form of item
 online
 Governing access note
 Access restricted to subscribing institution
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Note
 eBooks on EBSCOhost
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 Label
 Stochastic models with applications to genetics, cancers, AIDS, and other biomedical systems, by WaiYuan Tan, (electronic resource/)
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents

 Preface; 1 Introduction; 1.1. Some Basic Concepts of Stochastic Processes and Examples; 1.2. Markovian and NonMarkovian 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 HardyWeinberg law in population genetics; 2.11.1.1. The HardyWeinberg law for a single locus in diploid populations; 2.11.1.2. The HardyWeinberg 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 MetropolisHastings 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 ContinuousTime 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 ContinuousTime 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
 Control code
 ocn928387326
 Dimensions
 unknown
 Edition
 2nd edition.
 Extent
 1 online resource (access may be restricted)
 File format
 unknown
 Form of item
 online
 Governing access note
 Access restricted to subscribing institution
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Note
 eBooks on EBSCOhost
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
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