[SOLVED] CS代考计算机代写 Bayesian Bayesian Decision Theory

30 $

File Name: CS代考计算机代写_Bayesian_Bayesian_Decision_Theory.zip
File Size: 536.94 KB

SKU: 5253591204 Category: Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

Or Upload Your Assignment Here:


Bayesian Decision Theory
Two (complementary) hypotheses: H1 and H2. An event A.
Prior Odds: P(H1 ) Data:A Likelihood ratio: P(A| H1 )
Example
There are two types of coin: Type 1 coming up Heads with probability p1; Type 2 coming up Heads with probability p2. A coin is randomly selected; the probability it is a Type 1 coin is p. (The probability it’s a Type 2 coin is 1 – p. Be careful: p is a fundamentally different thing from p1 and p2.) The coin is then tossed n times, independently, resulting in the sequence R1 R2 R3 … Rn. For example if n = 6 one possible sequence is: H T H T H H. (The number of Heads in this sequence is X = 4.) The probability of this particular sequence is pi(1 – pi)pi(1 – pi)pipi =
p4 (1− p )2 where i = 1 or 2. All other sequences with 4 Hs and 2Ts have this probability. In ii
general then, if there are x Heads (and n – x Tails) in some particular sequence, the probability of that sequence is pix (1− pi )n−x . We now have:
P(H2 ) P(A| H2 )
Posterior Odds: P(H1 | A) =  P(H1 ) P(A| H1 ) = Prior Odds × Likelihood Ratio
   P(H |A) P(H )P(A|H )
2 22
PriorOdds Data Likelihood ratio
P(H1)= p P(H2 ) 1− p
A=RR R wherex oftheR areHeads 12ni
P(A|H) px(1−p)n−x 1 = 1 1
P(A|H2) px(1−p)n−x 22
P(H|A) P(H)P(A|H) p px(1−p)n−x PosteriorOdds 1=1 1=11
P(H2|A) P(H2)P(A|H2) 1−ppx(1−p)n−x 22
A reasonable strategy for deciding which coin has been selected is to predict it is a Type 1 coin if the posterior odds are greater than 1 (then P(H1|A) is above 1⁄2) and if not, predict it’s a Type 2 coin. (This is somewhat arbitrary should the posterior odds be exactly 1: this generally can’t happen. We will ignore the situation – there are ways to work around it – such as simply calling it a tie and refusing to make a decision.1)
p px(1−p)n−x Prediction: Type 1 coin if and only if 1 1 >1.
1− p px (1− p )n−x 22
1 When do we see posterior odds of 1? Try p = 0.5 and, take p1 = 0.3, p2 = 0.7 and suppose the tossing results in 10 Heads in 20 tosses. (You can sort of see why neither coin is preferred here.)

Reviews

There are no reviews yet.

Only logged in customers who have purchased this product may leave a review.

Shopping Cart
[SOLVED] CS代考计算机代写 Bayesian Bayesian Decision Theory
30 $