Get Discrete Probability Models and Methods: Probability on PDF

By Pierre Brémaud

ISBN-10: 3319434756

ISBN-13: 9783319434759

ISBN-10: 3319434764

ISBN-13: 9783319434766

The emphasis during this booklet is put on basic types (Markov chains, random fields, random graphs), common equipment (the probabilistic procedure, the coupling technique, the Stein-Chen strategy, martingale equipment, the strategy of varieties) and flexible instruments (Chernoff's sure, Hoeffding's inequality, Holley's inequality) whose area of software extends some distance past the current textual content. even though the examples handled within the booklet relate to the potential functions, within the verbal exchange and computing sciences, in operations examine and in physics, this booklet is within the first example excited by concept.

the extent of the e-book is that of a starting graduate path. it's self-contained, the necessities consisting in basic terms of uncomplicated calculus (series) and easy linear algebra (matrices). The reader isn't assumed to be taught in chance because the first chapters supply in enormous aspect the history essential to comprehend the remainder of the book.

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Additional resources for Discrete Probability Models and Methods: Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding

Sample text

EXERCISES 17 P (A ∪ B) = 1 − P (A ∩ B) and P (A ∪ B) = P (A) + P (B) − P (A ∩ B). 3. Urns 1. An urn contains 17 red balls and 19 white balls. Balls are drawn in succession at random and without replacement. What is the probability that the first 2 balls are red? 2. An urn contains N balls numbered from 1 to N . Someone draws n balls (1 ≤ n ≤ N ) simultaneously from the urn. What is the probability that the lowest number drawn is k? 4. About independence 1. Give a simple example of a probability space (Ω, F, P ) with three events A1 , A2 , A3 that are pairwise independent, but not globally independent (that is, the family {A1 , A2 , A3 } is not independent).

Define a random variable Xn by Xn (ω) = xn . It is the random number obtained at the n-th toss. It is indeed a random variable since for all an ∈ {0, 1}, {ω ; Xn (ω) = an } = {ω ; xn = an } ∈ F, by definition of F. The following are elementary remarks. 5 Let E and F be countable sets. Let X be a random variable with values in E, and let f : E → F be an arbitrary function. Then Y := f (X) is a random variable. Proof. Let y ∈ F . The set {ω; Y (ω) = y} is in F since it is a countable union of sets in F, namely: {Y = y} = {X = x} .

Such was the case in the historical experiments performed in 1865 by the Czech monk Gregory Mendel who studied the hereditary transmission of the nature of the skin in a species of green peas. The two alleles corresponding to the gene or character “nature of the skin” are a for “wrinkled” and A for “smooth”. The genes are grouped into pairs and there are two alleles. Therefore, three genotypes are possible for the character under study: aa, Aa (same as aA), and AA. During the reproduction process, each of the two parents contributes to the genetic heritage of their descendant by providing one allele of their pair.

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Discrete Probability Models and Methods: Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding by Pierre Brémaud


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