Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Particle filter-based Gaussian process optimisation for parameter
  inference   We propose a novel method for maximum likelihood-based parameter inference in
nonlinear and/or non-Gaussian state space models. The method is an iterative
procedure with three steps. At each iteration a particle filter is used to
estimate the value of the log-likelihood function at the current parameter
iterate. Using these log-likelihood estimates, a surrogate objective function
is created by utilizing a Gaussian process model. Finally, we use a heuristic
procedure to obtain a revised parameter iterate, providing an automatic
trade-off between exploration and exploitation of the surrogate model. The
method is profiled on two state space models with good performance both
considering accuracy and computational cost.

21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol