Session Overview
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

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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In general, one cannot add probabilities.
What is a Monte Carlo simulation?
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A simulation which arrives at an approximation of a probability by running many, many trials.
What is the guiding principle of inferential statistics?
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A random sample tends to exhibit the same properties as the population from which it is drawn.
What is the law of large numbers (a.k.a. Bernoulli's Law)?
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The law of large numbers basically says that using more test cases in a simulation involving randomness will increase our confidence in its results.
What is the gambler's fallacy?
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The belief that random numbers will even out constantly (e.g. that after a string of heads, it's “time for” the coin to come up tails.)
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 Nonrandom Problems.
Further Study
These optional resources are provided for students that wish to explore this topic more fully.
Readings
 Monte Carlo method. Wikipedia.