Session Overview
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This lecture starts with some examples of how to use pylab's plotting mechanisms. It then returns to the topic of using probability and statistics to derive information from samples. |
Session Activities
Lecture Videos
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Lecture 14: Sampling and Monte Carlo Simulation (00:50:52)
Lecture 14: Sampling and Monte Carlo Simulation
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About this Video
Topics covered: Plotting, randomness, probability, Pascal's algorithm, Monte Carlo simulation, inferential statistics, gambler's fallacy, law of large numbers.
Resources
Check Yourself
Can probabilities be added?
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What is a Monte Carlo simulation?
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What is the guiding principle of inferential statistics?
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What is the law of large numbers (a.k.a. Bernoulli's Law)?
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What is the gambler's fallacy?
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Problem Sets
Problem Set 6: Simulating Robots (Due)
In this problem set you will practice designing a simulation and implementing a program that uses classes.
- Instructions (PDF)
- Code Files (ZIP) (This ZIP file contains: 3 .py files.)
- Solutions (ZIP) (This ZIP file contains: 1 .py file.)
Problem Set 7 (Assigned)
Problem set 7 is assigned in this session. The instructions and solutions can be found on the session page when it is due, Lecture 16 Using Randomness to Solve Non-random Problems.
Further Study
These optional resources are provided for students that wish to explore this topic more fully.
Readings
- Monte Carlo method. Wikipedia.