豪华
发表于 2025-3-23 12:45:17
https://doi.org/10.1007/978-3-319-76433-7artificial intelligence; Bayesian; data science; inference; information; machine learning; minimum message
seruting
发表于 2025-3-23 14:31:03
978-3-030-09488-1Springer International Publishing AG, part of Springer Nature 2018
雪白
发表于 2025-3-23 18:06:44
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笼子
发表于 2025-3-24 01:30:35
Bits and Pieces,s, hints and tricks that may help the reader to get started at putting MML into practice. “Probability theory is nothing but common sense reduced to calculation” (Laplace) but data analysis software is numerical software and the results of computations need to be checked with scepticism, common sense and cunning.
Rustproof
发表于 2025-3-24 06:07:15
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Pruritus
发表于 2025-3-24 07:56:03
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补充
发表于 2025-3-24 12:20:33
https://doi.org/10.1007/978-94-017-9106-9(.) =∑. ⋅pr.(.) is also a model over the data-space. In particular, ∑.pr(.) = 1 for discrete data. . is a . , being a mixture of the . submodels, .. Similarly, if the . are models of continuous data defined by probability density functions pdf.(.) then . defined by pdf(.) =∑. ⋅pdf.(.) is a Mixture m
污秽
发表于 2025-3-24 16:54:57
https://doi.org/10.1007/978-94-017-9106-9atum is bivariate, . = 〈., .〉, although note that the input . and the output . can themselves be multivariate. Recall that the input data are common knowledge so a transmitter need not encode them in any message to a receiver and we can take it that pr(.) = 1. For a given function-model we are inter
Consensus
发表于 2025-3-24 22:07:57
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Felicitous
发表于 2025-3-25 03:14:41
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