Stochastic Processes Assignment Help

Stochastic Processes Assignment Help

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A stochastic process can be described as any process describing the evolution of a random phenomenon over time. The stochastic processes theory was settled in 1950 and since then, it has become a significant tool in mathematics, physics, and engineering.Stochastic processes are applied in various areas including modeling of stock pricing, differential geometry, and rational optional pricing.

Statistics Assignment Experts provide superior quality stochastic homework help to students facing challenges in the completion of stochastic projects. We have put together a great team of stochastic assignment helpers who have committed their lives to providing academic assistance services to campus students. But stochastic homework writing help is not the only service we offer on this website. We also provide assistance on any other subject covered under statistics in order to deliver an all-round academic help service to students. So whether you need help with stochastic assignments or any other projects related to statistics, we are the best company to provide it.

The Most Popular Process Concepts Involved In Stochastic Processes

There are many concepts covered in stochastic processes,  our incredible experts have delivered remarkable stochastic processes assignment help to students on all these concepts. Some of these include:

  • Markov processes: Named after Andrey Markov, Markov processes are random processes, in which the probability of an outcome of an event is based on the results of previous events. Markov processes are applied in many different areas in real life including studying and evaluating cruise control systems in vehicles, queuing customers who are arriving at an airport, analyzing currency exchange rates, and studying animal population dynamics to mention a few.
  • Brownian motion: Also known as pedesi, Brownian motion is the random movement of particles present in a fluid (gas or liquid) resulting from bombarding with the fast moving molecules in the fluid.
  • Poisson processes: These are a series of discrete events in which the average time between the events is known, whereas the exact timing of the events is random. The occurrence of an event doesn’t depend on the previous event. Events meet a Poisson criteria if they are not dependent of each other, the average rate is constant, and if no two events occur at the same time.
  • Gaussian processes: Gaussian processes are a powerful stochastic process for both regression and classification. Every finite collection of random variables in a Gaussian process has a multivariate normal distribution. This is named after Carl Gauss and is widely used in statistical modeling today.

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Applications Of Stochastic Processes

Stochastic processes have played a huge role in many engineering disciplines including:

  • Robotics
  • Power systems
  • Signal processing
  • Automotive technology
  • Semiconductor manufacturing
  • Manufacturing systems
  • Wireless networks
  • Communication networks

Among these applications, stochastic processes seem to be more prevalent in communication networks, which is what we are going to look intobriefly. There is a plenty of randomness and unpredictability in communication network. For starters, connections will come in and leave randomly. For a connection to be established, circuit switched networks will need to find enough resources, without which, a connection will be refused.

For internet connections and other packet switched networks, packets can be refused if adequate resources aren’t available. Generally, control in networks falls under four categories: admission control, congestion control, routing, and flow. For communication networks to function optimally, control strategies like stochastic processes must be applied. Other strategies that may be applied to ensure efficient performance of these networks include parameter estimation, simulation based estimation, and learning theory.

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Topics Administered By Our Stochastic Processes Homework Helpers

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  • Measure and probability
  • Discrete time martingales
  • Conditional expectation
  • Actuarial applications
  • Diffusion processes
  • Construction of diffusion
  • Stochastic integral functions
  • Gaussian additive processes
  • Brownian motion
  • Poisson processes
  • Dynkin’s formula
  • Markov analysis
  • Gamma process
  • Bernoulli process
  • Galton Watson process
  • Girsanov’s theorem
  • Cox process
  • Dirichlet processes
  • First passage time
  • Percolation theory
  • Ornstein Uhlenbeck process
  • Compound Poisson process
  • Levy process
  • Loop erased random walk
  • Probabilistic cellular automaton
  • Gaussian random field
  • Sample continuous process
  • Stochastic differential equation
  • Telegraph process
  • Stationary process

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