Here we do not show the derivation of Differential Chapman-Kolmogorov Equation, instead, we only show how to interpret the result. In the following sections, it is assumed that the stochastic process has Markov properties and the sample paths are always continuous and satisfy [Eq.1]:
which practically says that integrating all possibility of changes of states z to x (i.e. the “distance”) that are greater than δ with δ > 0, the result probability is zero and, thus, there are no possibility that there are any changes of states within an infinitesimal period of time Δt. This is also called the Lindeberg condition.
The Chapman-Kolomogov Equation defines, for a Markov Process, the conditional probabilities [Eq.2]:
Differential Chapman-Kolomogov Equation
W.r.t that, we requires the following properties in order to reduce [Eq.2] into a differential equation for all δ > 0 and denote condition |x-z| < δ as ε:
In the first requirement, W(z, t) is the jump term which describe discontinuous evolution for all +ve non-zero distance between x and z. Note that if W = 0, we restore [Eq.1] and the sample path is continuous.
In the second requirement, if δ limit to zero, the O(δ) term vanishes (i.e. continuous path), and the term A_i is often called the drift term because it suggest that E[dx] = A(x, t)dt, which can be interpret as the instantaneous rate of change of z..
In the third requirement, B(z, t) is often referred as the diffusion term, which can be seen as the instantaneous rate of change of covariance of all process close to z.
The full derivation will not be stated here, in fact it can be easily found on the internet by search the key words Differential Chapman-Kolmogorov. However, it is crucial to understand the origin of it and a brief introduction to the origin of differential Chapman-Kolmogorov equation will be written below referencing Crispin Gardiner’s book Stochastic methods: a handbook for the natural and social sciences.
First, we consider the time evolution of certain function f(z) which we required it to be twice differentiable. Then the instantaneous rate of change of expected value of this function can be written as:
[by first principle]
[by Chapman-Kolmogorov Equation]
Divide the integrals into two regions |x-z| > δ and |x-z| < δ and some manipulation, the result will be [Eq.3]:
this is the differential Chapman-Kolmogorov equation or sometimes called the master equation
Interpreting the equation
If we deliberately force the master equation to disobey [Eq.1], we will obtain a discontinuous process. For example, forcing both A(z, t) and B(z, t) to be zero, the differential equation is left to be:
Using initial condition , with delta being the Dirac-Delta function, the particular solution to the master equation is:
see that if we want to know the probability of staying in y after Δt, we substitute z as y and get the following equation [Eq.4]
Let’s break this down terms by terms so that it is easier to understand. Firstly, W(z|y, t) is the instantaneous probability (or probability per time) of going to state z from y, therefore, W(z|y, t)Δt is the accumulated probability of going to z from y within time period Δt. Secondly, the integral inside the square bracket denotes the total probability of leaving state y and going to any other states x, thus, one minus the integral is the total probability of staying in state y. In the case of [Eq.4], W(y|y, t)Δt is the probability of going from state y to state y meaning it includes the probability of first leaving state y then go back to y within Δt, and the integral represents ONLY staying in state y. Finally, the sum of both terms converge to the probability of staying in state y within Δt in [Eq.4].
The Fokker-Planck Equation is obtained when forcing the Jump term W in the master equation to become zero [Eq.5]
This equation is sometimes know as the diffusion process with drift term A and diffusion matrix B. Based on this equation, if we wish to compute , we can neglect the partial derivatives of A and B so that equation [Eq.5] becomes:
and solve this equation with the constrain (i.e. only one state exist at a time) and obtain:
which is, in fact, a Gaussian distribution with variance as matrix and mean .
Crispin W.. Gardiner. Stochastic methods: a handbook for the natural and social sciences. Springer, 2009.
Hierro, Juan, and César Dopazo. “Singular boundaries in the forward Chapman-Kolmogorov differential equation.” Journal of Statistical Physics 137.2 (2009): 305-329.