Lecture 17 - People @ EECS at UC Berkeley
... equally likely to land in any bin, regardless of what happens to the other balls. Here Ω = {(b 1 , b2 , . . . , b20 ) : 1 ≤ bi ≤ 10}; the component bi denotes the bin in which ball i lands. There are 1020 possible outcomes (why?), each with probability 10120 . More generally, if we throw m balls int ...
... equally likely to land in any bin, regardless of what happens to the other balls. Here Ω = {(b 1 , b2 , . . . , b20 ) : 1 ≤ bi ≤ 10}; the component bi denotes the bin in which ball i lands. There are 1020 possible outcomes (why?), each with probability 10120 . More generally, if we throw m balls int ...
Solutions to problems 1-25
... Three prisoners, A, B, and C, are held in separate cells. Two are to be executed. The warder knows specifically who is to be executed, and who is to be freed, whereas the prisoners know only that two are to be executed. Prisoner A reasons as follows: my probability of being freed is clearly 13 until ...
... Three prisoners, A, B, and C, are held in separate cells. Two are to be executed. The warder knows specifically who is to be executed, and who is to be freed, whereas the prisoners know only that two are to be executed. Prisoner A reasons as follows: my probability of being freed is clearly 13 until ...
Problem Solving Probability Games Fractions
... Fiona spins a spinner with shapes like this 100 times and recorded this table of outcomes. Where could the lines go on the spinner to make this table of outcomes likely? Create your own problem! Now solve it! ...
... Fiona spins a spinner with shapes like this 100 times and recorded this table of outcomes. Where could the lines go on the spinner to make this table of outcomes likely? Create your own problem! Now solve it! ...
Excel Version
... variable can have an infinite number of values i.e. in binomials our variable had limited possible values ...
... variable can have an infinite number of values i.e. in binomials our variable had limited possible values ...
Bayesian, Likelihood, and Frequentist Approaches to Statistics
... P(eHB ) is defined by the problem. Indeed, it is equal to (1/4)/ (2/4)=1/2. The ratio of likelihoods is thus one to two comparing urn A to urn B or two to one in favor of urn B. This quantity is then perfectly objective. The Bayesian will counter that this may well be so but it still fails to captur ...
... P(eHB ) is defined by the problem. Indeed, it is equal to (1/4)/ (2/4)=1/2. The ratio of likelihoods is thus one to two comparing urn A to urn B or two to one in favor of urn B. This quantity is then perfectly objective. The Bayesian will counter that this may well be so but it still fails to captur ...
Probability File
... elementary events for an experiment. For example, for the die-tossing experiment, the set of events consists of 1, 2, 3, 4, 5, and 6. The set is collectively exhaustive because it includes all possible outcomes. Thus, all sample spaces are collectively exhaustive. Complementary Events (Ac): The comp ...
... elementary events for an experiment. For example, for the die-tossing experiment, the set of events consists of 1, 2, 3, 4, 5, and 6. The set is collectively exhaustive because it includes all possible outcomes. Thus, all sample spaces are collectively exhaustive. Complementary Events (Ac): The comp ...
PPTX
... A binomial expression is the sum of two terms, such as (a + b). Now consider (a + b)2 = (a + b)(a + b). When expanding such expressions, we have to form all possible products of a term in the first factor and a term in the second factor: (a + b)2 = a·a + a·b + b·a + b·b Then we can sum identical ter ...
... A binomial expression is the sum of two terms, such as (a + b). Now consider (a + b)2 = (a + b)(a + b). When expanding such expressions, we have to form all possible products of a term in the first factor and a term in the second factor: (a + b)2 = a·a + a·b + b·a + b·b Then we can sum identical ter ...
