Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Informed Sub-Sampling MCMC: Approximate Bayesian Inference for Large
  Datasets   This paper introduces a framework for speeding up Bayesian inference
conducted in presence of large datasets. We design a Markov chain whose
transition kernel uses an (unknown) fraction of (fixed size) of the available
data that is randomly refreshed throughout the algorithm. Inspired by the
Approximate Bayesian Computation (ABC) literature, the subsampling process is
guided by the fidelity to the observed data, as measured by summary statistics.
The resulting algorithm, Informed Sub-Sampling MCMC (ISS-MCMC), is a generic
and flexible approach which, contrary to existing scalable methodologies,
preserves the simplicity of the Metropolis-Hastings algorithm. Even though
exactness is lost, i.e. the chain distribution approximates the posterior, we
study and quantify theoretically this bias and show on a diverse set of
examples that it yields excellent performances when the computational budget is
limited. If available and cheap to compute, we show that setting the summary
statistics as the maximum likelihood estimator is supported by theoretical
arguments.

25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph