---
title: "Markov process"
firstLetter: "M"
publishDate: 2021-11-14
categories:
- Mathematics
- Stochastic analysis

date: 2021-11-13T21:05:21+01:00
draft: false
markup: pandoc
---

# Markov process

Given a [stochastic process](/know/concept/stochastic-process/)
$\{X_t : t \ge 0\}$ on a filtered probability space
$(\Omega, \mathcal{F}, \{\mathcal{F}_t\}, P)$,
it is said to be a **Markov process**
if it satisfies the following requirements:

1.  $X_t$ is $\mathcal{F}_t$-adapted,
    meaning that the current and all past values of $X_t$
    can be reconstructed from the filtration $\mathcal{F}_t$.
2.  For some function $h(x)$,
    the [conditional expectation](/know/concept/conditional-expectation/)
    $\mathbf{E}[h(X_t) | \mathcal{F}_s] = \mathbf{E}[h(X_t) | X_s]$,
    i.e. at time $s \le t$, the expectation of $h(X_t)$ depends only on the current $X_s$.
    Note that $h$ must be bounded and *Borel-measurable*,
    meaning $\sigma(h(X_t)) \subseteq \mathcal{F}_t$.

This last condition is called the **Markov property**,
and demands that the future of $X_t$ does not depend on the past,
but only on the present $X_s$.

If both $t$ and $X_t$ are taken to be discrete,
then $X_t$ is known as a **Markov chain**.
This brings us to the concept of the **transition probability**
$P(X_t \in A | X_s = x)$, which describes the probability that
$X_t$ will be in a given set $A$, if we know that currently $X_s = x$.

If $t$ and $X_t$ are continuous, we can often (but not always) express $P$
using a **transition density** $p(s, x; t, y)$,
which gives the probability density that the initial condition $X_s = x$
will evolve into the terminal condition $X_t = y$.
Then the transition probability $P$ can be calculated like so,
where $B$ is a given Borel set (see [$\sigma$-algebra](/know/concept/sigma-algebra/)):

$$\begin{aligned}
    P(X_t \in B | X_s = x)
    = \int_B p(s, x; t, y) \dd{y}
\end{aligned}$$

A prime examples of a continuous Markov process is
the [Wiener process](/know/concept/wiener-process/).
Note that this is also a [martingale](/know/concept/martingale/):
often, a Markov process happens to be a martingale, or vice versa.
However, those concepts are not to be confused:
the Markov property does not specify *what* the expected future must be,
and the martingale property says nothing about the history-dependence.



## References
1.  U.H. Thygesen,
    *Lecture notes on diffusions and stochastic differential equations*,
    2021, Polyteknisk Kompendie.