منابع مشابه
Notes on Gaussian processes and majorizing measures
Consider a Gaussian process {Xt}t∈T for some index set T . This is a collection of jointly Gaussian random variables, meaning that every finite linear combination of the variables has a Gaussian distribution. We will additionally assume that the process is centered, i.e. E(Xt) = 0 for all t ∈ T . It is well-known that such a process is completely characterized by the covariances {E(XsXt)}s,t∈T ...
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In these notes, we will talk about a different flavor of learning algorithms, known as Bayesian methods. Unlike classical learning algorithm, Bayesian algorithms do not attempt to identify “best-fit” models of the data (or similarly, make “best guess” predictions for new test inputs). Instead, they compute a posterior distribution over models (or similarly, compute posterior predictive distribu...
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ژورنال
عنوان ژورنال: The Annals of Mathematical Statistics
سال: 1972
ISSN: 0003-4851
DOI: 10.1214/aoms/1177690865