Is there a way to compute transition operators for Markov processes? To ask something much more tractable, suppose I have an Ito diffusion $$dX\_t \ = \ \sigma(X\_t) dB\_t \ + \ b(X\_t) dt$$


(or given by its generator). Is there any way to write down the transition kernels for the process $X\_t$ from the above SDE? Also, maybe with assumptions on the coefficients $\sigma, b$, is there any way to establish any niceness in the sense of $L^p(\mathbb{R})$ for the transition kernels given just the diffusion? I'm guessing the drift component gives Dirac masses along some curve defined by $b(x)$ and the diffusion component gives a Gaussian process, but I'm not seeing quite how to write down the kernels themselves...


Thanks!!


Quick edit: I know one can solve the corresponding Fokker-Planck equation (with a given initial condition) with Green's functions, Fourier series, etc., but is there another way to detect any properties like $L^p$-regularity without PDE methods? 



Computing transition operators for Markov processes
**1) Explicit expressions for transition densities**


In the case of linear systems with additive noise of the form


$d X(t) = (A X\_t + b) \, d t + \sigma \, d B\_t$ 


it is possible to obtain an explicit solution, see 
*Ioannis Karatzas and Steven E. Shreve*, MR 1241411 [**Brownian motion and stochastic calculus**](http://dx.doi.org/10.1007/978-1-4684-0302-2), Section 5.6.


There are a few other explicitly solvable cases (in terms of transition density), for example the SDE for the Bessel process


$d X\_t = (d-1)/(2X\_t) \, d t + B\_t$,


and the exponential Brownian motion,


$d X\_t = a X\_t \, d t + \sigma X\_t \, d B\_t$,


and quite a few other examples. In general finding the transition density of an SDEs means that you have solved the associated Fokker-Planck equation (equivalently known as forward Kolmogorov equation) which is a parabolic PDE. So finding transition densities is in general of the same difficulty as solving a linear PDE of a particular type, and solving such PDEs explicitly is typically not possible.


Also noteworthy is the Lamperti transform to change from general one-dimensional diffusion coefficients to a diffusion $Z$ with unit diffusion coefficient, by defining $Z\_t = \varphi(X\_t)$ where $\varphi(x) = \int^x \frac{1}{\sigma(\xi)} \ d \xi$.


I would recommend to consult a basic textbook focussing on explicit computations such as Øksendal's and look at the exercises to get a feel for what is possible in this direction.


**2) Regularity of solutions** 


Of course, if $\sigma = 0$, your solution will be a deterministic trajectory, with corresponding Dirac measure as transition kernel, i.e. not belonging to any $L^p$. A simple and well known result is that if $\sigma(x)$ is globally bounded away from 0 (known as ellipticity), you will have smooth transition densities. The important generalization of this result for multi-dimensional SDEs where some of the components of $\sigma$ may be zero is given by Hörmander's theorem. I recommend Martin Hairer's lecture notes, [*Convergence of Markov processes*](http://www.hairer.org/notes/Convergence.pdf), for a gentle introduction. 


Once you establish the probability density is smooth (using ellipticity/Hörmander) and vanishing at infinity (corresponding to boundedness in probability), it will be bounded and therefore in $L^p(\mathbb R)$ for any $p \geq 1$. Boundedness in probability can be established using e.g. bounds on $\mathbb E|X(t)|$, see the book by Xuerong Mao, or various other sources.


