Stochastic Processes Assignment Homework Help

Stochastic Process is a collection of random variables representing the evolution of some system of random values over time. This is the probabilistic counterpart to a deterministic process. It can also be defined as statistical process involving a number of random variables depending on a variable parameter. Statisticsonlineassignmenthelp assures to provide you with well-structured and well-formatted Stochastic Process solutions and our deliveries have always been on time whether it’s a day’s deadline or long. You can anytime buy Stochastic Process assignments, Stochastic Process Homeworks, Stochastic Process online tutoring through us and we assure to build your career with success and prosperity.

The team has helped a number of students pursuing education through regular and online universities, institutes or online Tutoring in the following topics-

• Absorption Probabilities
• Application: Birth-death chains
• Application: Queueing chains and branching process
• Applications of Ito Calculus to Finance
• Average number of visits to a recurrent state
• Axioms of probability
• Backward Kolmogorov and the Fokker-Planck equations
• Basic theory: Forward and backward equations, Poisson process, Birth-death processes
• binomial, Poisson, and geometric distribution
• Birth processes, death processes
• Bistability, metastability and exit problems
• Branching processes
• Brownian motion
• Calculations involving independent random variables
• Chapman-Kolmogorov equations
• Conditional Expectations, Filtration and Martingales
• conditional probability and independence
• connection between SDEs and the Fokker-Planck equation
• Continuous random variables: probability density and cumulative density functions
• Continuous time Markov processes
• Convergence to stationary distribution: period, aperiodicity, and Perron-Frobenius theorem
• Decomposing state space: Irreducible closed sets and stationary distributions
• Decomposition of state space
• Discrete time Markov processes
• Discrete time, continuous time
• Elements of probability theory
• Fluid Model of a G/G/1 Queueing System
• Functional Strong Law of Large Numbers and Functional Central Limit Theorem
• G/G/1 in Heavy-traffic; Introduction to Queueing Networks
• G/G/1 Queueing Systems and Reflected Brownian Motion (RBM)
• Gaussian processes: Definitions and some examples
• Introduction to stochastic integrals and differential equations
• Ito and Stratonovich stochastic integrals
• Ito Process-Ito Formula
• Jump processes: Definitions and examples of jump processes, Markov jump processes
• Large Deviations for i.i.d. Random Variables
• linear algebra interpretation for finite-state chains
• little renewal theory
• Long term behavior
• Stochastic Processes Monte Carlo (MCMC)
• Markov chains on discrete spaces
• Martingale Property of Ito Integral and Girsanov Theorem
• Martingales and Stopping Times
• Measure Theory
• Modes of Convergence and Convergence Theorems
• Null and positive recurrence and stationary distributions
• Numerical methods for SDEs
• Poisson processes
• Probability Basics: Probability Space, s-algebras, Probability Measure
• Probability on Metric Spaces
• Properties of birth-death processes
• Properties of the Distribution Function G
• Quadratic Variation Property of Brownian Motion
• Random Variables and Measurable Functions; Strong Law of Large Numbers (SLLN)
• Random Variables: Discrete random variables and probability distribution functions
• Recurrence, transience, and stationary distributions
• Reflection Principle; The Distribution of the Maximum; Brownian Motion with Drift
• Renewal theory
• Residual and total life Discrete renewal processes
• Second-order and gaussian processes
• s-fields on Measure Spaces and Weak Convergence
• Spatially extended systems
• State Space Structure: Recurrence and transience,
• Stationary distributions: Definition and basic properties
• Stationary gaussian processes and linear time-invariant systems
• Stochastic differential equations (SDEs)
• Stochastic mode elimination
• Strong Markov Property
• Transition matrix
• Uniform, exponential, and normal densities
• Wiener processes