10.2 Properties of PDF and CDF for Continuous Ran
... by whether or not the endpoints are included or excluded. • When we work with continuous random variables, it is usually not necessary to be precise about specifying whether or not a range of numbers includes the endpoints. This is quite different from the situation we encounter with discrete random ...
... by whether or not the endpoints are included or excluded. • When we work with continuous random variables, it is usually not necessary to be precise about specifying whether or not a range of numbers includes the endpoints. This is quite different from the situation we encounter with discrete random ...
Chapter 3 More about Discrete Random Variables
... • In many problems, the quantity of interest can be expressed in the form Y = X1 + · · · + Xn , where the Xi are independent Berboulli(p) random variables. • The random variable Y is called a binomial(n, p) random variable. • Its probability mass function is µ ¶ n k pY (k) = p (1 − p)n−k , k ...
... • In many problems, the quantity of interest can be expressed in the form Y = X1 + · · · + Xn , where the Xi are independent Berboulli(p) random variables. • The random variable Y is called a binomial(n, p) random variable. • Its probability mass function is µ ¶ n k pY (k) = p (1 − p)n−k , k ...
Machine Learning
... P(I,C) = P(I=True, C=True) • 30 like chocolate but not ice cream P(I’,C) = P(I=False, C=True) • 5 like ice cream but not chocolate P(I,C’) • 10 don’t like chocolate nor ice cream Prob(I) = P(I=True) Prob(C) = P(C=True) Prob(I,C) = P(I=True, C=True) ...
... P(I,C) = P(I=True, C=True) • 30 like chocolate but not ice cream P(I’,C) = P(I=False, C=True) • 5 like ice cream but not chocolate P(I,C’) • 10 don’t like chocolate nor ice cream Prob(I) = P(I=True) Prob(C) = P(C=True) Prob(I,C) = P(I=True, C=True) ...
