Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sequentially guided MCMC proposals for synthetic likelihoods and
  correlated synthetic likelihoods   Synthetic likelihood (SL) is a strategy for parameter inference when the
likelihood function is analytically or computationally intractable. In SL, the
likelihood function of the data is replaced by a multivariate Gaussian density
over summary statistics of the data. SL requires simulation of many replicate
datasets at every parameter value considered by a sampling algorithm, such as
Markov chain Monte Carlo (MCMC), making the method computationally-intensive.
We propose two strategies to alleviate the computational burden. First, we
introduce an algorithm producing a proposal distribution that is sequentially
tuned and made conditional to data, thus it rapidly \textit{guides} the
proposed parameters towards high posterior density regions. In our experiments,
a small number of iterations of our algorithm is enough to rapidly locate high
density regions, which we use to initialize one or several chains that make use
of off-the-shelf adaptive MCMC methods. Our "guided" approach can also be
potentially used with MCMC samplers for approximate Bayesian computation (ABC).
Second, we exploit strategies borrowed from the correlated pseudo-marginal MCMC
literature, to improve the chains mixing in a SL framework. Moreover, our
methods enable inference for challenging case studies, when the posterior is
multimodal and when the chain is initialised in low posterior probability
regions of the parameter space, where standard samplers failed. To illustrate
the advantages stemming from our framework we consider five benchmark examples,
including estimation of parameters for a cosmological model and a stochastic
model with highly non-Gaussian summary statistics.

5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an