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on 18-Jun-2020 (Thu)

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An excellent way to make code more maintainable is through loose coupling. As far back as 1994, when the Gang of Four wrote Design Patterns, this was already common knowledge: 2 Program to an interface, not an implementation.
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for MCMC, we allow this candidate generating density (or proposal density) to depend upon the current state of the chain, and we denote it by q(xt , y)
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The optimal form for the acceptance function, in the sense that suitable candidates are rejected least often and computational efficiency is maximised, is given by .
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Flashcard 5539446263052

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It can be shown that the optimal form for the acceptance function, in the sense that [...], is given by

Answer
suitable candidates are rejected least often and computational efficiency is maximised

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It can be shown that the optimal form for the acceptance function, in the sense that suitable candidates are rejected least often and computational efficiency is maximised, is given by

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Flashcard 5539448622348

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The optimal form for the acceptance function, in the sense that suitable candidates are rejected least often and computational efficiency is maximised, is given by [...]
Answer
.

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The optimal form for the acceptance function, in the sense that suitable candidates are rejected least often and computational efficiency is maximised, is given by .

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The Metropolis Hastings algorithm has the advantage over the Gibbs Sampler, in that it is not necessary to know all of the conditional distributions, we need only simulate from q, which we can choose arbitrarily. Moreover, and this can be of crucial importance , we only need to know π up to proportionality, since any constants of proportionality cancel in the numerator and denominator of the calculation of α. The price for this simplicity is that if q is poorly chosen, then the numbe r of rejections can be high, so that the efficiency of the procedure can be low.
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The single-update Metropolis Hastings algorithm is the Metropolis Hastings algorithm, updating variables one-at-a-time.
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the Gibbs Sampler is a special case of the single-update Metropolis Hastings algorithm
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Flashcard 5539458059532

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The [...] is the Metropolis Hastings algorithm, updating variables one-at-a-time.
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single-update Metropolis Hastings algorithm

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The single-update Metropolis Hastings algorithm is the Metropolis Hastings algorithm, updating variables one-at-a-time.

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Flashcard 5539459632396

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The single-update Metropolis Hastings algorithm is [...].
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the Metropolis Hastings algorithm, updating variables one-at-a-time

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The single-update Metropolis Hastings algorithm is the Metropolis Hastings algorithm, updating variables one-at-a-time.

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Flashcard 5539461205260

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[...] is a special case of the single-update Metropolis Hastings algorithm
Answer
the Gibbs Sampler

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the Gibbs Sampler is a special case of the single-update Metropolis Hastings algorithm

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Flashcard 5539462778124

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the Gibbs Sampler is a special case of [...]
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the single-update Metropolis Hastings algorithm

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Given a set of Markov kernels, each with invariant distribution π , it can be shown that, under weak conditions on the kernels, a hybrid algorithm consisting of mixtures or cycles of these kernels will produce a chain with the desired stationary distribution.
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In a mixture of Markov kernels, probabilities are specified and, at each step, one of the kernels is selected according to these probabilities
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In a cycle of Markov kernels, each of the kernels are used in a pre-determined sequence which is repeated until the sampler is stopped
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Flashcard 5539480341772

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ing and studying those flash cards.
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[default - edit me]

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8 Better Ways to Make and Study Flash Cards
kanji. Doing so helped me learn very quickly. You’re probably studying a subject right now that would benefit from flash card study as well, so it’d be useful to learn the best practices for mak<span>ing and studying those flash cards. In this article, I’ll go over 8 of those best practices. Let’s get into it. 1. Make Your Own Flash Cards Lots of people like to share their flash card decks, and there are also plenty o