Random variables, probability distributions, expected value, variance, binomial distribution, Gaussian distribution (normal distribution)
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1 Random variables, probability distributions, expected value, variance, binomial distribution, Gaussian distribution (normal distribution) Relevant Readings: Table 5.2, Section 5.3 up through in Mitchell CS495 - Machine Learning, Fall 2009
2 Prob and stats Probability, statistics, and sampling theory aren t only just useful in ML. These are generally useful in computer science (such is in the study of randomized algorithms)
3 Prob and stats Probability, statistics, and sampling theory aren t only just useful in ML. These are generally useful in computer science (such is in the study of randomized algorithms)
4 Some basics A random variable is a numerical outcome of some random experiment
5 Some basics A random variable is a numerical outcome of some random experiment It can be thought of as an unknown value that changes every time it is observed
6 Some basics A random variable is a numerical outcome of some random experiment It can be thought of as an unknown value that changes every time it is observed Similar to Math.random()
7 Some basics A random variable is a numerical outcome of some random experiment It can be thought of as an unknown value that changes every time it is observed Similar to Math.random() Example: The result of rolling a die
8 Some basics A random variable is a numerical outcome of some random experiment It can be thought of as an unknown value that changes every time it is observed Similar to Math.random() Example: The result of rolling a die A random variable s behavior is governed its probability distribution
9 Some basics A random variable is a numerical outcome of some random experiment It can be thought of as an unknown value that changes every time it is observed Similar to Math.random() Example: The result of rolling a die A random variable s behavior is governed its probability distribution The probability distribution is a specification for the likelihood of the random variable taking on different values
10 Some basics A random variable is a numerical outcome of some random experiment It can be thought of as an unknown value that changes every time it is observed Similar to Math.random() Example: The result of rolling a die A random variable s behavior is governed its probability distribution The probability distribution is a specification for the likelihood of the random variable taking on different values Example: For rolling a die, the probability distribution is uniform, meaning that each possibility (1, 2, 3, 4, 5, 6) has equal probability of coming up (1/6).
11 Some basics A random variable is a numerical outcome of some random experiment It can be thought of as an unknown value that changes every time it is observed Similar to Math.random() Example: The result of rolling a die A random variable s behavior is governed its probability distribution The probability distribution is a specification for the likelihood of the random variable taking on different values Example: For rolling a die, the probability distribution is uniform, meaning that each possibility (1, 2, 3, 4, 5, 6) has equal probability of coming up (1/6). Random variables can be discrete or continuous
12 Some basics A random variable is a numerical outcome of some random experiment It can be thought of as an unknown value that changes every time it is observed Similar to Math.random() Example: The result of rolling a die A random variable s behavior is governed its probability distribution The probability distribution is a specification for the likelihood of the random variable taking on different values Example: For rolling a die, the probability distribution is uniform, meaning that each possibility (1, 2, 3, 4, 5, 6) has equal probability of coming up (1/6). Random variables can be discrete or continuous The probability that random variable X takes on the value x is denoted Pr(X = x)
13 Some basics A random variable is a numerical outcome of some random experiment It can be thought of as an unknown value that changes every time it is observed Similar to Math.random() Example: The result of rolling a die A random variable s behavior is governed its probability distribution The probability distribution is a specification for the likelihood of the random variable taking on different values Example: For rolling a die, the probability distribution is uniform, meaning that each possibility (1, 2, 3, 4, 5, 6) has equal probability of coming up (1/6). Random variables can be discrete or continuous The probability that random variable X takes on the value x is denoted Pr(X = x)
14 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2]
15 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)?
16 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero.
17 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)?
18 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)? Zero.
19 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)? Zero. What is Pr(1/4 X 1/2)?
20 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)? Zero. What is Pr(1/4 X 1/2)? 1/8
21 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)? Zero. What is Pr(1/4 X 1/2)? 1/8 So then for continuous random variables, we work in terms of ranges of values, rather than the probability of equaling some value exactly
22 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)? Zero. What is Pr(1/4 X 1/2)? 1/8 So then for continuous random variables, we work in terms of ranges of values, rather than the probability of equaling some value exactly Think of it this way: what s the probability of throwing a dart and hitting exactly the right spot on the wall?
23 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)? Zero. What is Pr(1/4 X 1/2)? 1/8 So then for continuous random variables, we work in terms of ranges of values, rather than the probability of equaling some value exactly Think of it this way: what s the probability of throwing a dart and hitting exactly the right spot on the wall? Zero.
24 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)? Zero. What is Pr(1/4 X 1/2)? 1/8 So then for continuous random variables, we work in terms of ranges of values, rather than the probability of equaling some value exactly Think of it this way: what s the probability of throwing a dart and hitting exactly the right spot on the wall? Zero. But after it s thrown, it actually hit some exact spot.
25 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)? Zero. What is Pr(1/4 X 1/2)? 1/8 So then for continuous random variables, we work in terms of ranges of values, rather than the probability of equaling some value exactly Think of it this way: what s the probability of throwing a dart and hitting exactly the right spot on the wall? Zero. But after it s thrown, it actually hit some exact spot. Therefore zero-probability doesn t necessarily mean impossible
26 Continuous probability distributions Consider a uniform continuous random variable X over the range [0,2] What is Pr(X = 1/2)? Zero. What is Pr(X = 1/4)? Zero. What is Pr(1/4 X 1/2)? 1/8 So then for continuous random variables, we work in terms of ranges of values, rather than the probability of equaling some value exactly Think of it this way: what s the probability of throwing a dart and hitting exactly the right spot on the wall? Zero. But after it s thrown, it actually hit some exact spot. Therefore zero-probability doesn t necessarily mean impossible
27 Some basic notions Summation notation: n i=0 a i means a 0 + a a n
28 Some basic notions Summation notation: n i=0 a i means a 0 + a a n The expected value (aka. mean) of a random variable Y is E[Y ] = i y i Pr(Y = y i ), where the sum is over all possible outcomes
29 Some basic notions Summation notation: n i=0 a i means a 0 + a a n The expected value (aka. mean) of a random variable Y is E[Y ] = i y i Pr(Y = y i ), where the sum is over all possible outcomes Just a weighted average (the continuous case would be an integral).
30 Some basic notions Summation notation: n i=0 a i means a 0 + a a n The expected value (aka. mean) of a random variable Y is E[Y ] = i y i Pr(Y = y i ), where the sum is over all possible outcomes Just a weighted average (the continuous case would be an integral). The variance of a random variable Y is Var(Y ) = E[(Y E[Y ]) 2 ]
31 Some basic notions Summation notation: n i=0 a i means a 0 + a a n The expected value (aka. mean) of a random variable Y is E[Y ] = i y i Pr(Y = y i ), where the sum is over all possible outcomes Just a weighted average (the continuous case would be an integral). The variance of a random variable Y is Var(Y ) = E[(Y E[Y ]) 2 ] This is a measure of how much Y deviates from the mean
32 Some basic notions Summation notation: n i=0 a i means a 0 + a a n The expected value (aka. mean) of a random variable Y is E[Y ] = i y i Pr(Y = y i ), where the sum is over all possible outcomes Just a weighted average (the continuous case would be an integral). The variance of a random variable Y is Var(Y ) = E[(Y E[Y ]) 2 ] This is a measure of how much Y deviates from the mean The standard deviation of a random variable Y is simply Var(Y )
33 Some basic notions Summation notation: n i=0 a i means a 0 + a a n The expected value (aka. mean) of a random variable Y is E[Y ] = i y i Pr(Y = y i ), where the sum is over all possible outcomes Just a weighted average (the continuous case would be an integral). The variance of a random variable Y is Var(Y ) = E[(Y E[Y ]) 2 ] This is a measure of how much Y deviates from the mean The standard deviation of a random variable Y is simply Var(Y )
34 Discrete uniform distribution Suppose X is a discrete random variable with uniform probability distribution over the set {1, 2, 3,..., m}
35 Discrete uniform distribution Suppose X is a discrete random variable with uniform probability distribution over the set {1, 2, 3,..., m} What is E[X ]?
36 Discrete uniform distribution Suppose X is a discrete random variable with uniform probability distribution over the set {1, 2, 3,..., m} What is E[X ]? (m + 1)/2
37 Discrete uniform distribution Suppose X is a discrete random variable with uniform probability distribution over the set {1, 2, 3,..., m} What is E[X ]? (m + 1)/2 What is Var[X ]?
38 Discrete uniform distribution Suppose X is a discrete random variable with uniform probability distribution over the set {1, 2, 3,..., m} What is E[X ]? (m + 1)/2 What is Var[X ]? (m 2 1)/12
39 Discrete uniform distribution Suppose X is a discrete random variable with uniform probability distribution over the set {1, 2, 3,..., m} What is E[X ]? (m + 1)/2 What is Var[X ]? (m 2 1)/12
40 Continuous uniform distribution Suppose X is a continuous random variable with uniform probability distribution over the interval [a, b]
41 Continuous uniform distribution Suppose X is a continuous random variable with uniform probability distribution over the interval [a, b] What is E[X ]?
42 Continuous uniform distribution Suppose X is a continuous random variable with uniform probability distribution over the interval [a, b] What is E[X ]? (a + b)/2
43 Continuous uniform distribution Suppose X is a continuous random variable with uniform probability distribution over the interval [a, b] What is E[X ]? (a + b)/2 What is Var[X ]?
44 Continuous uniform distribution Suppose X is a continuous random variable with uniform probability distribution over the interval [a, b] What is E[X ]? (a + b)/2 What is Var[X ]? (b a) 2 /12
45 Continuous uniform distribution Suppose X is a continuous random variable with uniform probability distribution over the interval [a, b] What is E[X ]? (a + b)/2 What is Var[X ]? (b a) 2 /12
46 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads
47 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads A binomial distribution is: how many times did we get heads?
48 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads A binomial distribution is: how many times did we get heads? Is this a discrete or continuous distribution?
49 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads A binomial distribution is: how many times did we get heads? Is this a discrete or continuous distribution? Discrete
50 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads A binomial distribution is: how many times did we get heads? Is this a discrete or continuous distribution? Discrete Suppose X is a random variable with binomial probability distribution
51 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads A binomial distribution is: how many times did we get heads? Is this a discrete or continuous distribution? Discrete Suppose X is a random variable with binomial probability distribution What is E[X ]?
52 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads A binomial distribution is: how many times did we get heads? Is this a discrete or continuous distribution? Discrete Suppose X is a random variable with binomial probability distribution What is E[X ]? np
53 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads A binomial distribution is: how many times did we get heads? Is this a discrete or continuous distribution? Discrete Suppose X is a random variable with binomial probability distribution What is E[X ]? np What is Var[X ]?
54 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads A binomial distribution is: how many times did we get heads? Is this a discrete or continuous distribution? Discrete Suppose X is a random variable with binomial probability distribution What is E[X ]? np What is Var[X ]? np(1 p)
55 Binomial distribution Imagine an unfair coin n times that has probability p of landing on heads A binomial distribution is: how many times did we get heads? Is this a discrete or continuous distribution? Discrete Suppose X is a random variable with binomial probability distribution What is E[X ]? np What is Var[X ]? np(1 p)
56 Gaussian (normal) distribution If you add lots of random variables together, the sum is in itself a random variable whose probability distribution tends to look like a Gaussian.
57 Gaussian (normal) distribution If you add lots of random variables together, the sum is in itself a random variable whose probability distribution tends to look like a Gaussian. Thus, Gaussian distributions show up a lot in nature
58 Gaussian (normal) distribution If you add lots of random variables together, the sum is in itself a random variable whose probability distribution tends to look like a Gaussian. Thus, Gaussian distributions show up a lot in nature It s a bell shaped curve
59 Gaussian (normal) distribution If you add lots of random variables together, the sum is in itself a random variable whose probability distribution tends to look like a Gaussian. Thus, Gaussian distributions show up a lot in nature It s a bell shaped curve The probability distribution for a Gaussian with mean µ and variance σ 2 is defined as:
60 Gaussian (normal) distribution If you add lots of random variables together, the sum is in itself a random variable whose probability distribution tends to look like a Gaussian. Thus, Gaussian distributions show up a lot in nature It s a bell shaped curve The probability distribution for a Gaussian with mean µ and variance σ 2 is defined as: e (x µ)2 /(2σ 2) /(σ 2π)
61 Gaussian (normal) distribution If you add lots of random variables together, the sum is in itself a random variable whose probability distribution tends to look like a Gaussian. Thus, Gaussian distributions show up a lot in nature It s a bell shaped curve The probability distribution for a Gaussian with mean µ and variance σ 2 is defined as: e (x µ)2 /(2σ 2) /(σ 2π)
62 Application Suppose we are programming Naive Bayes and want to handle a continuous variable
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64 Application Suppose we are programming Naive Bayes and want to handle a continuous variable One way we could do this: measure the mean and variance of the relevant examples, model them with a probability distribution (such as a Gaussian), and use that distribution to help determine whether yes or no is more likely for the given instance
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