What is non markovian?

Non-Markovian dynamics constitute any interaction between a system and its environment which then affects the system at a later time; the environment need not even be coherent.

What is the difference between Markov chain and Markov process?

A Markov chain is a discrete-time process for which the future behaviour, given the past and the present, only depends on the present and not on the past. A Markov process is the continuous-time version of a Markov chain.

What are Markov processes used for?

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. Predicting traffic flows, communications networks, genetic issues, and queues are examples where Markov chains can be used to model performance.

What defines a Markov process?

A Markov process is a random process in which the future is independent of the past, given the present. Thus, Markov processes are the natural stochastic analogs of the deterministic processes described by differential and difference equations. They form one of the most important classes of random processes.

What is a recurrent state?

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 a non-Markovian process?

A non-Markovian process is a stochastic process that does not exhibit the Markov property. The Markov property, sometimes known as the memoryless property, states that the conditional probability of a future state is only dependent on the present state (and is independent of any prior state).

What is non-Markovian return of quantum information?

Often the environment exhibits a memory of the system’s state, resulting in Non-Markovian return of quantum information to the system at later times.

Is Markov property not being followed?

Clearly, Markov property is not being followed and the task is very, very difficult. When is adoption of non-random sampling method advocated? What are the different non- random sampling methods with examples?

What is an example of a Markov chain?

A common example used in books introducing Markov chains is that of the weather — say that the chance that it will be sunny, cloudy, or rainy tomorrow depends only on what the weather is today, independent of past weather conditions. If we relaxed this last assumption and said that the weather is dependent