MIT OpenCourseWare

Browse materialsMIT 16.322 · Fall 2004

Stochastic Estimation and Control

Subject: Statistics

Art: 1783 balloonj, 1786 · Public domain

Instructor: Prof. Wallace Vander Velde
Term: Fall 2004
Level: Graduate
What's inside: 1 lecture note · 1 reading · 25 other files · 3 web pages
Materials open: MIT OCW

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Source: MIT OpenCourseWare 16.322 Stochastic Estimation and Control, Fall 2004, Prof. Wallace Vander Velde — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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A lecture sequence of 26 items is confirmed. Assignment and exam materials exist too, but MIT's own order for them could not be verified, so they're shown as materials below rather than a sequence.

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Lecture Notes

25 files

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MIT OpenCourseWare, Prof. Wallace Vander Velde, 16.322 Stochastic Estimation and Control, Fall 2004.

License: CC BY-NC-SA 4.0 · Edition: Fall 2004 · Open the original course on OCW ↗

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Stochastic Estimation and Control

Before you start

What's here, and what isn't

Not included here

Materials by section

Lecture Notes

Assignments

Lecture 1: Introduction Random Signals Intuitive Notion of Probability Axiomatic Probability Joint and Conditional Probability

Lecture 2: Independence Random Variables Probability Distribution and Density Functions

Lecture 3: Expectation, Averages and Characteristic Function Normal or Gaussian Random Variables Impulsive Probability Density Functions Multiple Random Variables

Lecture 4: Correlation, Covariance, and Orthogonality Sum of Independent Random Variables and Tendency Toward Normal Distribution Transformation of Random Variables

Lecture 5: Some Common Distributions

Lecture 6: More Common Distributions Multivariate Normal Density Function Linear Transformation and General Properties of Normal Random Variables

Lecture 7: Linearized Error Propagation

Lecture 8: More Linearized Error Propagation

Lecture 9: Concept of a Random Process Probabilistic Description of a Random Process Gaussian Random Process Stationarity, Ergodicity, and Classification of Processes

Lecture 10: Autocorrelation Function Crosscorrelation Function

Lecture 11: Power Spectral Density Function Cross Spectral Density Function White Noise

Lecture 12: Gauss-Markov Process Random Telegraph Wave Wiener or Brownian-Motion Process

Lecture 13: Determination of Autocorrelation and Spectral Density Functions from Experimental Data

Lecture 14: Introduction: The Analysis Problem Stationary (Steady-State) Analysis Integral Tables for Computing Mean-Square Value

Lecture 15: Pure White Noise and Bandlimited Systems Noise Equivalent Bandwidth Shaping Filter

Lecture 16: Nonstationary (Transient) Analysis - Initial Condition Response Nonstationary (Transient) Analysis - Forced Response

Lecture 17: The Wiener Filter Problem Optimization with Respect to a Parameter

Lecture 18: The Stationary Optimization Problem - Weighting Function Approach Orthogonality

Lecture 19: Complementary Filter Perspective

Lecture 20: Estimation A Simple Recursive Example

Lecture 21: Markov Processes

Lecture 22: State Space Description Vector Description of a Continuous-Time Random Process Discrete-Time Model

Lecture 23: Monte Carlo Simulation of Discrete-Time Systems The Discrete Kalman Filter Scalar Kalman Filter Examples

Lecture 24: Transition from the Discrete to Continuous Filter Equations Solution of the Matrix Riccati Equation

Lecture 25: Divergence Problems

Lecture 26: Complementary Filter Methodology INS Error Models Damping the Schuler Oscillation with External Velocity Reference Information