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Markov process also known as the Markoff process is the stochastic process that is used to satisfy a particular property called the Markov property. The name of this process is given after the Great Russian Mathematician, Andrey Markov. This process is considered memoryless. To be honest, the property of Markov would be met only if one can predict the future of the process on the basis of the current state as well as when one knows about the history, i.e. the condition of the present state, future, and past that are not dependent on each other. Markov processes are the most advanced concepts in statistics and comprise affine processes, diffusion, Feller, and other concepts. Our adept professionals have sound knowledge of all the concepts to cater to your assignment needs. Our experts are available round the clock to assist students at different academic levels on their assignments.

This is an intriguing topic whose concepts are used in different fields like economics, finance, games, speech recognition, music, etc. The Markov process is the toughest topic in statistics many students find it hard to understand and score poor grades.

Markov analysis is a technique that is used to analyze the behavior of the variable available in the project at present or forecast the behavior in the future. This method is used in making different decisions. From the day it was coined, this has been used by many businesses to improve its effectiveness.

A few of the topics on which our statistics experts offer help include:

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Finite State Markov Chains | Poisson Processes |

Rates of convergence to Stationarity | Classification of States |

Piecewise deterministic Markov Processes | Random walks in one, two, and three dimensions |

Birth-and-Death Processes | Calculation of n-step Transition Probabilities |

Feller processes | Random Walk on Finite Group |

Reversibility and the M/M/1 queue | Markov Property |

Convergence to Equilibrium for Ergodic Chains | Dirichlet Form and Spectral Gap Methods |

Stationary Distribution | Affine processes. |

Diffusion processes | Hitting Probabilities and Mean-Hitting Times |

Reversibility and Ehrenfest’s Urn Model | Coupling Methods with Applications |