Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic derivatives and generalized h-transforms of Markov processes   Let $R$ be a continuous-time Markov process on the time interval $[0,1]$ with
values in some state space $X$. We transform this reference process $R$ into
$P:=f(X_0)\exp (-\int_0^1 V_t(X_t) dt) g(X_1)\,R$ where $f,g$ are nonnegative
measurable functions on X and V is some measurable function on $[0,1]\times X$.
It is easily seen that $P$ is also Markov. The aim of this paper is to identify
the Markov generator of $P$ in terms of the Markov generator of $R$ and of the
additional ingredients: $f,g$ and $V$ in absence of regularity assumptions on
$f,g$ and $V.$ As a first step, we show that the extended generator of a Markov
process is essentially its stochastic derivative. Then, we compute the
stochastic derivative of $P$ to identify its generator, under a finite entropy
condition. The abstract results are illustrated with continuous diffusion
processes on $\mathbb{R}^d$ and Metropolis algorithms on a discrete space.

23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
1
The categories of this paper are: cs
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph