Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Conditional Density Filtering   We propose a Conditional Density Filtering (C-DF) algorithm for efficient
online Bayesian inference. C-DF adapts MCMC sampling to the online setting,
sampling from approximations to conditional posterior distributions obtained by
propagating surrogate conditional sufficient statistics (a function of data and
parameter estimates) as new data arrive. These quantities eliminate the need to
store or process the entire dataset simultaneously and offer a number of
desirable features. Often, these include a reduction in memory requirements and
runtime and improved mixing, along with state-of-the-art parameter inference
and prediction. These improvements are demonstrated through several
illustrative examples including an application to high dimensional compressed
regression. Finally, we show that C-DF samples converge to the target posterior
distribution asymptotically as sampling proceeds and more data arrives.

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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin