Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A comparison of nonlinear population Monte Carlo and particle Markov
  chain Monte Carlo algorithms for Bayesian inference in stochastic kinetic
  models   In this paper we address the problem of Monte Carlo approximation of
posterior probability distributions in stochastic kinetic models (SKMs). SKMs
are multivariate Markov jump processes that model the interactions among
species in biochemical systems according to a set of uncertain parameters.
Markov chain Monte Carlo (MCMC) methods have been typically preferred for this
Bayesian inference problem. Specifically, the particle MCMC (pMCMC) method has
been recently shown to be an effective, while computationally demanding, method
applicable to this problem. Within the pMCMC framework, importance sampling
(IS) has been used only as the basis of the sequential Monte Carlo (SMC)
approximation of the acceptance ratio in the Metropolis-Hastings kernel.
However, the recently proposed nonlinear population Monte Carlo (NPMC)
algorithm, based on an iterative IS scheme, has also been shown to be effective
as a Bayesian inference tool for low dimensional (predator-prey) SKMs. In this
paper, we provide an extensive performance comparison of pMCMC versus NPMC,
when applied to the challenging prokaryotic autoregulatory network. We show how
the NPMC method can greatly outperform the pMCMC algorithm in this scenario,
with an overall moderate computational effort. We complement the numerical
comparison of the two techniques with an asymptotic convergence analysis of the
nonlinear IS scheme at the core of the proposed method when the importance
weights can only be computed approximately.

22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th