## What does it mean for a Markov chain to be time homogeneous?

Definition. A Markov chain is called homogeneous if and only if the transition. probabilities are independent of the time t, that is, there exist. constants Pi,j such that. Pi,j “ PrrXt “ j | Xt´1 “ is holds for all times t.

**What are the key features of Markov chains?**

The defining characteristic of a Markov chain is that no matter how the process arrived at its present state, the possible future states are fixed. In other words, the probability of transitioning to any particular state is dependent solely on the current state and time elapsed.

**How do you prove a Markov chain is time homogeneous?**

A Markov process is time homogeneous if P ( X s + t ∈ A ∣ X s = x ) = P ( X t ∈ A ∣ X 0 = x ) for every s , t ∈ T , x ∈ S and A ∈ S . So if is homogeneous (we usually don’t bother with the time adjective), then the process { X s + t : t ∈ T } given X s = x is equivalent (in distribution) to the process { X t : t ∈ T } …

### What is a transient state in Markov chain?

A recurrent state has the property that a Markov chain starting at this state returns to this state infinitely often, with probability 1. A transient state has the property that a Markov chain starting at this state returns to this state only finitely often, with probability 1.

**What is the purpose of Markov chains?**

Markov chains are among the most important stochastic processes. They are stochastic processes for which the description of the present state fully captures all the information that could influence the future evolution of the process.

**What are Markov chains good for?**

Markov Chains are exceptionally useful in order to model a discrete-time, discrete space Stochastic Process of various domains like Finance (stock price movement), NLP Algorithms (Finite State Transducers, Hidden Markov Model for POS Tagging), or even in Engineering Physics (Brownian motion).

#### How do I know if my Markov chain is irreducible?

The Markov chain mc is irreducible if every state is reachable from every other state in at most n – 1 steps, where n is the number of states ( mc. NumStates ). This result is equivalent to Q = (I + Z)n – 1 containing all positive elements. I is the n-by-n identity matrix.

**Are Markov chain deterministic?**

In these algorithms, the state of the Markov process evolves according to a deterministic dynamics which is modified using a Markov transition kernel at random event times.

**Does Markov chain always reach steady state?**

No. Some Markov chains reach a state of equilibrium but some do not. Some Markov chains transitions do not settle down to a fixed or equilibrium pattern.