Probability & Statistical Inference · Stochastic Processes

Stochastic processes · Advanced topics and applications in statistics

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Advanced Analysis and Statistical Applications

Overview

Topics covered

  • Introduction
  • Stochastic Processes Advanced Topics
  • Diffusions and Stochastic Differential Equations
  • Existence and Uniqueness of Solutions
  • Kolmogorov and Fokker-Planck equations
  • Continuous Time Martingale Theory

Related concepts

Main connections

  • Calculus & differentiation
  • Maximum likelihood estimation
  • Markov chain Monte Carlo (MCMC)
  • Variance & standard deviation
  • Normal (Gaussian) distribution
  • Poisson distribution

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

dXt=μ(t,Xt)dt+σ(t,Xt)dWt,X0=ξ,
View LaTeX source\dd X_t = \mu(t, X_t) \dd t + \sigma(t, X_t) \dd W_t, \qquad X_0 = \xi,