from Foundations of Markov chain Monte
نویسندگان
چکیده
The aim of this course is to address the complexity of counting and sampling problems from an algorithmic perspective. Typically we will be interested in counting the size of a collection of combinatorial structures of a graph, e.g., the number of spanning trees of a graph. We will soon see that this style of counting problem is intimately related to the sampling problem which asks for a random structure from this collection, e.g., generate a random spanning tree. The course will demonstrate that the class of counting and sampling problems can be addressed in a very cohesive and elegant (at least to my tastes) manner.
منابع مشابه
Cs294-2 Markov Chain Monte Carlo: Foundations & Applications 2.1 Applications of Markov Chain Monte Carlo (continued) 2.1.1 Statistical Inference
where Pr(Θ) is the prior distribution and refers to the information previously known about Θ, Pr(X | Θ) is the probability that X is obtained with the assumed model, and Pr(X) is the unconditioned probability that X is observed. Pr(Θ | X) is commonly called the posterior distribution and can be written in the form π(Θ) = w(Θ)/Z, where the weight w(Θ) = Pr(X | Θ)Pr(Θ) is easy to compute but the ...
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