Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximate Maximum A Posteriori Inference with Entropic Priors   In certain applications it is useful to fit multinomial distributions to
observed data with a penalty term that encourages sparsity. For example, in
probabilistic latent audio source decomposition one may wish to encode the
assumption that only a few latent sources are active at any given time. The
standard heuristic of applying an L1 penalty is not an option when fitting the
parameters to a multinomial distribution, which are constrained to sum to 1. An
alternative is to use a penalty term that encourages low-entropy solutions,
which corresponds to maximum a posteriori (MAP) parameter estimation with an
entropic prior. The lack of conjugacy between the entropic prior and the
multinomial distribution complicates this approach. In this report I propose a
simple iterative algorithm for MAP estimation of multinomial distributions with
sparsity-inducing entropic priors.

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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: nlin
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: mtrl-th