Markov Processes and Learning Models
β Scribed by M. Frank Norman (Eds.)
- Publisher
- Academic Press
- Year
- 1972
- Tongue
- English
- Leaves
- 289
- Series
- Mathematics in Science and Engineering 84
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
Content:
Edited by
Page iii
Copyright page
Page iv
Dedication
Page v
Preface
Pages xi-xiii
0 Introduction
Pages 1-17
1 Markov Processes and Random Systems with Complete Connections
Pages 21-29
2 Distance Diminishing Models and Doeblin-Fortet Processes
Pages 30-42
3 The Theorem of Ionescu Tulcea and Marinescu, and Compact Markov Processes
Pages 43-65
4 Distance Diminishing Models with Noncompact State Spaces
Pages 66-72
5 Functions of Markov Processes
Pages 73-97
6 Functions of Events
Pages 98-105
7 Introduction to Slow Learning
Pages 109-115
8 Transient Behavior in the Case of Large Drift
Pages 116-136
9 Transient Behavior in the Case of Small Drift
Pages 137-151
10 Steady-State Behavior
Pages 152-162
11 Absorption Probabilities
Pages 163-171
12 The Five-Operator Linear Model
Pages 175-194
13 The Fixed Sample Size Model
Pages 195-208
14 Additive Models
Pages 209-224
15 Multiresponse Linear Models
Pages 225-233
16 The Zeaman-House-Lovejoy Models
Pages 234-243
17 Other Learning Models
Pages 244-256
18 Diffusion Approximation in a Genetic Model and a Physical Model
Pages 257-262
References
Pages 263-267
List of Symbols
Pages 269-270
Index
Pages 271-274
π SIMILAR VOLUMES
Markov processes are used to model systems with limited memory. They are used in many areas including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, popula
<p>This book presents an algebraic development of the theory of countable state space Markov chains with discrete- and continuous-time parameters. A Markov chain is a stochastic process characterized by the Markov propΒ erty that the distribution of future depends only on the current state, not on t