M06.5 CONNECT THE MECHANISM
Track a hidden state from noisy observations
The filter that helped navigate Apollo to the Moon fits in five lines. Learn how it blends a prediction with a noisy reading, and why it beats either one alone.
LESSON OVERVIEW12 min lesson
Lesson overview
The filter that helped navigate Apollo to the Moon fits in five lines. Learn how it blends a prediction with a noisy reading, and why it beats either one alone.
What you’ll explore
- Hidden Markov models and Kalman filters alternate prediction and evidence updates; their state, transition, and observation assumptions determine what they can estimate.
GO TO THE SOURCE
Original explanations, connected to the research.
Artificial Intelligence: A Modern Approach — authors’ materialsA New Approach to Linear Filtering and Prediction Problems (Kalman, 1960)Suggest a correction
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