Skip to main content
Graph
Search
fr
|
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Brownian Motion: Fundamentals and Applications
Graph Chatbot
Related lectures (31)
Previous
Page 2 of 4
Next
Stochastic Processes and Spectral Densities
Covers spectral densities, signal correlations, and white noise processes in stochastic systems.
Parseval Relations and Fourier Transform
Covers Parseval relations, Fourier series, energy calculation, and correlation in signal processing.
Probability and Stochastic Processes: Fundamentals and Applications
Discusses the fundamentals of probability and stochastic processes, focusing on random variables, their properties, and applications in statistical signal processing.
Properties of Time-Frequency Domain Signals
Covers the main properties of time-frequency domain signals and their limitations.
Stochastic Calculus: Brownian Motion
Explores stochastic processes in continuous time, emphasizing Brownian motion and related concepts.
Stochastic Calculus: Itô's Formula
Covers Stochastic Calculus, focusing on Itô's Formula, Stochastic Differential Equations, martingale properties, and option pricing.
Yule Walker Equations: Efficient Implementation and Correlation Analysis
Explores Yule Walker equations for efficient implementation and correlation analysis in signal processing.
Stochastic Calculus: Integrals and Processes
Explores stochastic calculus, emphasizing integrals, processes, martingales, and Brownian motion.
Martingales and Brownian Motion
Discusses convergence, martingales, Brownian motion, joint laws, testing procedures, and stop times.
Signal Processing: Correlation and Spectral Density
Delves into correlation and spectral density of signals, explaining their significance and applications.