MIT15_097S12_lec15

Now lets plug k py j py j

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Unformatted text preview: always accept the transition θ → θ' . 25 If p(θ' |y ) is less than p(θ|y ), then for every accepted draw θ, we should have � y on average p((θ ||y)) accepted draws of θ' . We thus accept the transition with pθ probability probability 6.2.2 p(θ� |y ) p(θ|y ) . Thus for any transition, we accept the transition with p(θ' |y ) min , 1 . p(θ|y ) (24) Steps of the algorithm We now give the steps of the Metropolis-Hastings algorithm. Step 1. Choose a starting point θ0 . Set t = 1. Step 2. Draw θ∗ from the proposal distribution J (θt−1 , ·). The proposed move for time t is to move from θt−1 to θ∗ . Step 3. Compute the following: α(θ t−1 p(θ∗ |y )J (θ∗ , θt−1 ) , θ ) := min ,1 p(θt−1 |y )J (θt−1 , θ∗ ) p(y |θ∗ )p(θ∗ )J (θ∗ , θt−1 ) = min ,1 p(y |θt−1 )p(θt−1 )J (θt−1 , θ∗ ) ∗ (25) We’ll explain this more soon. The fact that we can compute ratios of posterior probabilities without having to worry about the normalization integ...
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This note was uploaded on 03/24/2014 for the course MIT 15.097 taught by Professor Cynthiarudin during the Spring '12 term at MIT.

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