Markov Chains Assignment Homework Help

A Markov Chain is a random process that undergoes transitions from one state to another on a state space. A Markov chain is a stochastic process with the property that, conditioned on its present state, its future states are independent of the past states. It must possess a property that is usually characterized as "memorylessness": the probability distribution of the next state depends only on the current state and not on the sequence of events that preceded it. Markov chains have many applications as statistical models of real-world processes. Markov chains are related to Brownian motion and the ergodic hypothesis. Statisticsonlineassignmenthelp assures to provide you with well-structured and well-formatted solutions and our deliveries have always been on time whether it’s a day’s deadline or long. You can anytime buy assignments online through us and we assure to build your career with success and prosperity.

Following is the list of comprehensive topics in which we offer the quality solutions:

• Convergence to equilibrium for ergodic chains
• Equivalence of positive recurrence and the existence of an invariant distribution
• Aperiodic chains
• Convergence to equilibrium *and proof by coupling*
• Discrete-Time Markov Chains
• Discrete-Time Chains
• Recurrence and Transience
• Periodicity
• Invariant and Limiting Distributions
• Time Reversal
• Hidden Markov model
• Introduction to classical Markov chain analysis; total variation mixing and coupling methods
• Invariant distributions : Notation, Stationary distributions, Equilibrium distributions
• Markov decision process
• Markov chains for the evolution of spin-systems and the interplay between dynamical and static phase transitions.
• Path Coupling and log-Sobolev inequalities
• Perfect simulation
• Quantum Markov chain
• Survival probability for birth and death chains, stopping times and strong Markov property
• Survival probability for birth death chains
• Mean hitting times are minimal solutions to RHEs
• Stopping times
• Strong Markov property
• Recurrence and transience
• Recurrence and transience
• Equivalence of recurrence and certainty of return
• Equivalence of transience and summability of n-step transition probabilities
• Recurrence as a class property
• Relation with closed classes
• Special Discrete-Time Chains
• The Ehrenfest Chains
• The Bernoulli-Laplace Chain
• Reliability Chains
• The Branching Chain
• Queuing Chains
• Birth Death Chains
• Random Walks on Graphs
• Semi-Markov process
• Telescoping Markov chain
• Variable-order Markov model Hitting probabilities and mean hitting times
• Absorption probabilities and mean hitting times
• Calculation of hitting probabilities and mean hitting times
• Absorption probabilities are minimal solutions to RHEs
• Gambler’s ruin
• Strong stationary times; Markov chains on the symmetric group
• The Cutoff phenomenon for Markov chains; random walks on expander graphs