Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exact Bayesian inference for discretely observed Markov Jump Processes
  using finite rate matrices   We present new methodologies for Bayesian inference on the rate parameters of
a discretely observed continuous-time Markov jump processes with a countably
infinite state space. The usual method of choice for inference, particle Markov
chain Monte Carlo (particle MCMC), struggles when the observation noise is
small. We consider the most challenging regime of exact observations and
provide two new methodologies for inference in this case: the minimal extended
state space algorithm (MESA) and the nearly minimal extended state space
algorithm (nMESA). By extending the Markov chain Monte Carlo state space, both
MESA and nMESA use the exponentiation of finite rate matrices to perform exact
Bayesian inference on the Markov jump process even though its state space is
countably infinite. Numerical experiments show improvements over particle MCMC
of between a factor of three and several orders of magnitude.

3
The categories of this paper are: physics
1
The categories of this paper are: cs
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int