What is the difference between discrete event simulation and Monte Carlo simulation?

Monte Carlo simulation is appropriate for static systems that do not involve the passage of time. Discrete-event simulation is appropriate for dynamic systems where the passage of time plays a significant role.

What discrete event simulation means?

Discrete event simulation (DES) is a method used to model real world systems that can be decomposed into a set of logically separate processes that autonomously progress through time. Each event occurs on a specific process, and is assigned a logical time (a timestamp).

What is the difference between Markov model and hidden Markov model?

Markov model is a state machine with the state changes being probabilities. In a hidden Markov model, you don’t know the probabilities, but you know the outcomes.

What are the different approaches used to describe the discrete simulation?

System modeling approaches: Finite-state machines and Markov chains. Stochastic process and a special case, Markov process. Queueing theory and in particular birth–death process.

What is Markov model used for what are its applications?

A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable. For example, let us consider the speech recognition problem, for which HMMs have been extensively used for several decades [1].

How does a discrete event simulation model progress?

Converserly, a discrete event simulation model progresses according to health events, which can happen at various times.

What is the difference between a Markov model and a microsimulation model?

A Markov cohort model is “memoryless,” while a microsimulation model is not subject to this limitation. “Memoryless” is a defining feature of a Markov model, and indicates that the transition probabilities do not depend on history.

What is the difference between Markov chain and Markov process models?

In a Markov chain model, the probability of an event remains constant over time. In the example above, the probability of moving from uncontrolled diabetes to controlled diabetes would be the same across all model cycles, even as the cohort ages. In a Markov process model, the probability of an event can change over time.

How can I reify time in a Markov model?

A Markov model, on the other hand, does not give you tools to do this. The only way time is reified in the Markov model is in the transition function between states—you get a sequence of states in the order they were simulated and nothing else.