Conditional Probability Distribution : Random variable, Probability distribution, Discrete probability distribution, Conditional probability, Continuous probability distribution

Agnes F. Vandome

ISBN 10: 6132659854 / ISBN 13: 9786132659859

Published by Omniscriptum, 2010

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Please note that the content of this book primarily consists of articlesavailable from Wikipedia or other free sources online. Given two jointlydistributed random variables X and Y, the conditional probabilitydistribution of Y given X is the probability distribution of Y when X isknown to be a particular value. The concept of the conditionaldistribution of a continuous random variable is not as intuitive as itmight seem: Borel's paradox shows that conditional probability densityfunctions need not be invariant under coordinate transformations. If fordiscrete random variables P(Y = y | X = x) = P(Y = y) for all x and yor for continuous random variables fY(y | X=x) = fY(y) for all x and ythen Y is said to be independent of X.

Seller Inventory # 9786132659859

Bibliographic Details

• Title: Conditional Probability Distribution : Random variable, Probability distribution, Discrete probability distribution, Conditional probability, Continuous probability distribution
• Publisher: Omniscriptum
• Publication Date: 2010
• Language: English
• ISBN 10: 6132659854
• ISBN 13: 9786132659859
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 125 grams

About this title

Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Given two jointly distributed random variables X and Y, the conditional probability distribution of Y given X is the probability distribution of Y when X is known to be a particular value. The concept of the conditional distribution of a continuous random variable is not as intuitive as it might seem: Borel''s paradox shows that conditional probability density functions need not be invariant under coordinate transformations. If for discrete random variables P(Y = y | X = x) = P(Y = y) for all x and y, or for continuous random variables fY(y | X=x) = fY(y) for all x and y, then Y is said to be independent of X.