Revisiting Maximum-A-Posteriori Estimation in Log-Concave Models
نویسندگان
چکیده
منابع مشابه
Maximum likelihood estimation of a multi- dimensional log-concave density
Let X1, . . . ,Xn be independent and identically distributed random vectors with a (Lebesgue) density f. We first prove that, with probability 1, there is a unique log-concave maximum likelihood estimator f̂n of f. The use of this estimator is attractive because, unlike kernel density estimation, the method is fully automatic, with no smoothing parameters to choose. Although the existence proof ...
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ژورنال
عنوان ژورنال: SIAM Journal on Imaging Sciences
سال: 2019
ISSN: 1936-4954
DOI: 10.1137/18m1174076