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Optimal estimation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2011) 6925 LNAI 37
DOI: 10.1007/978-3-642-24412-4_4
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Abstract

Data Y = {yt : t = 1, 2, . . ., n}, or Y|X = {(yt, x 1,t, x2,t, . . .)}, X explanatory variables. Want to learn properties in Y expressed by set of distributions as models: f(Y|X s;θ, s), where θ = θ1, . . . , θk(s) real-valued parameters, s structure parameter: for picking the most important variables in X. © 2011 Springer-Verlag.

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Rissanen, J. (2011). Optimal estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6925 LNAI, p. 37). https://doi.org/10.1007/978-3-642-24412-4_4

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