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About Stochastic Processes and its Industrial Applications

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About Stochastic Processes and its Industrial Applications

Statistics Homework Tutors has a pool of statistics homework tutors and statistics homework experts who can serve to your total requirements in Stochastic Processes such as Stochastic Processes homework help, Stochastic Processes Assignment help, Stochastic Processes project help, Stochastic Processes term paper help and Stochastic Processes Exam preparation help. Our team of talented and qualified Stochastic Processes experts and tutors are always ready to complete your assignments and they serve you the best.

We provide timely help to students in Stochastic Processes at affordable prices with best response to your assignments, Stochastic Processes Distributions homework, Stochastic Processes research paper writing, Stochastic Processes research critique and Stochastic Processes case studies so that students can understand their assignments better apart from having answers. Our highly qualified & dedicated Stochastic Processes experts and tutors are available to help students in their Stochastic Processes assignments in every possible way.


The Basic Topics That Are Normally Considered Part Of College Industrial Applications Of Stochastic Processes That We Can Help With:

  • Arithmetic random variables
  • Blackwell model of dynamic programming
  • Brownian motion and diffusions
  • Continuity & Convergence of random variables
  • Doob’s convergence theorem
  • Doubly stochastic Poisson process
  • Khintchine’s law of the iterated logarithm
  • L´evy’s modulus of continuity
  • L´evy’s upward and downward theorems
  • Little's formula, Markovian queues in tandem
  • Markov chains in discrete and continuous times
  • Markovian models: sibmating, Moran, Wright-Fisher
  • Point Processes: Renewal process, filtered point/Poisson process
  • Queueing Processes: Markovian queuing model, queues with finite capacity, Little's formula, tandem queues
  • Random Jacobi matrices
  • Self-intersection of Brownian motion
  • Stochastic differential equations
  • The Wiener measure
  • Time series models and their use in forecasting
  • Wright-Fisher model with varying generation sizes, Kimura models, hidden Markov models
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