Advanced Mathematical Tools for Automatic Control Engineers: by Alexander S. Poznyak (Auth.) PDF

By Alexander S. Poznyak (Auth.)

ISBN-10: 0080446736

ISBN-13: 9780080446738

Content material:
Dedication

, Page ii
Copyright

, Page iv
Preface

, Pages xv-xix
Notations and Symbols

, Pages xxi-xxv
Chapter 1 - likelihood Space

, Pages 3-31
Chapter 2 - Random Variables

, Pages 33-45
Chapter three - Mathematical Expectation

, Pages 47-62
Chapter four - uncomplicated Probabilistic Inequalities

, Pages 63-81
Chapter five - attribute Functions

, Pages 83-99
Chapter 6 - Random Sequences

, Pages 103-131
Chapter 7 - Martingales

, Pages 133-173
Chapter eight - restrict Theorems as Invariant Laws

, Pages 175-236
Chapter nine - uncomplicated homes of continuing Time Processes

, Pages 239-261
Chapter 10 - Markov Processes

, Pages 263-286
Chapter eleven - Stochastic Integrals

, Pages 287-322
Chapter 12 - Stochastic Differential Equations

, Pages 323-354
Chapter thirteen - Parametric Identification

, Pages 357-416
Chapter 14 - Filtering, Prediction and Smoothing

, Pages 417-437
Chapter 15 - Stochastic Approximation

, Pages 439-470
Chapter sixteen - strong Stochastic Control

, Pages 471-527
Bibliography

, Pages 529-533
Index

, Pages 535-538

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Extra resources for Advanced Mathematical Tools for Automatic Control Engineers: Stochastic Techniques

Sample text

5. 33) 55 Mathematical expectation fξ (x) x a b Fig. 1. A uniform distribution. Proof. 31) gives N (n) E{ξ(ω)} = lim n→∞ xk,n P{ω : ξn (ω) = xk,n } k=1 N (n) = lim n→∞ P{ω : x j ≤ ω ≤ x j + xk,n xj+ N (n) = lim n→∞ f ξ (u)du j N (n) j = xk,n } j,n xk,n k=1 = lim n→∞ j,n |ξn (ω) j k=1 xj +∞ xk,n f ξ (xk,n ) k=1 j,n x f ξ (x)d x = −∞ Lemma is proven. 3. (A uniform distribution) In this situation a random variable ξ(ω) has the density (see Fig. 34) 56 Advanced Mathematical Tools for Automatic Control Engineers: Volume 2 fξ (x) x a Fig.

1. (the toss of a die) The sample space in this case is = {ω} = {1; 2; 3; 4; 5; 6} The measure is given by P{ω = i} = 1 6 ∀i = 1, . . , 6 Take x(ω) = ω. 2. (Bernoulli’s variable) Bernoulli’s variable is a binary variable defined by x(ω) = 1 with the probability p 0 with the probability q where q = 1 − p ∈ [0, 1]. 4. 29) 54 Advanced Mathematical Tools for Automatic Control Engineers: Volume 2 Proof. 9), which monotonically from below converges to ξ(ω). 28). Lemma is proven. 5. 33) 55 Mathematical expectation fξ (x) x a b Fig.

Transformation of distributions . . . . . . . . . . . . . . . Continuous random variables . . . . . . . . . . . . . . . 33 37 42 In this chapter a connection between measure theory and the basic notion of probability theory – a random variable – is established. In fact, random variables are the functions from the probability space to some other measurable space. The definition of a random variable as a measurable function is presented. Several simple examples of random variables are considered.

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Advanced Mathematical Tools for Automatic Control Engineers: Stochastic Techniques by Alexander S. Poznyak (Auth.)


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