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Probability Theory: Counting in Terms of Proportions

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77 diapositives · 914 Views · Sep 23, 2014

This document explores counting in terms of proportions and probability distributions, particularly focusing on the heights of descendants from an initial ancestor, Adam. Through an unbiased binomial distribution, the study illustrates how the heights vary and how these distributions converge toward a Gaussian shape as the sample size increases. Additionally, it describes the concept of coin flipping in Manhattan as a random walk, discussing the probability of returning to a specific point after a series of steps. This intersection of probability theory and practical examples enhances the understanding of statistical concepts.

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  1. Probability Theory:Counting in Terms of Proportions

  2. A Probability Distribution Proportion of MALES HEIGHT

  3. The Descendants Of Adam • Adam was X inches tall. • He had two sons • One was X+1 inches tall • One was X-1 inches tall • Each of his sons had two sons ….

  4. X+1 X-1 X X+2 X-2 X-1 X+1 X+3 X-3 X-2 X-4 X X+4 X+2 X 1 1 5 5 10 10 6 6 15 15 20

  5. 1 X+1 X-1 X X+2 X-2 X-1 X+1 X+3 X-3 X-2 X-4 X X+4 X+2 1 1 5 5 10 10 6 6 15 15 20

  6. 1 1 1 X X+2 X-2 X-1 X+1 X+3 X-3 X-2 X-4 X X+4 X+2 1 1 5 5 10 10 6 6 15 15 20

  7. 1 1 1 2 1 1 X-1 X+1 X+3 X-3 X-2 X-4 X X+4 X+2 1 1 5 5 10 10 6 6 15 15 20

  8. 1 1 1 2 1 1 3 3 1 1 X-2 X-4 X X+4 X+2 1 1 5 5 10 10 6 6 15 15 20

  9. 1 1 1 2 1 1 3 3 1 1 4 1 6 1 4 1 1 5 5 10 10 6 6 15 15 20

  10. 1 1 1 2 1 1 3 3 1 1 4 1 6 1 4 1 1 5 5 10 10 6 6 15 15 20 In nth generation, there will be 2n males, each with one of n+1 different heights: h0< h1 < . . .< hn. hi = (X – n + 2i) occurs with proportion

  11. Unbiased Binomial Distribution On n+1 Elements. • Let S be any set {h0, h1, …, hn} where each element hi has an associated probability Any such distribution is called an Unbiased Binomial Distribution or an Unbiased Bernoulli Distribution.

  12. As the number of elements gets larger, the shape of the unbiased binomial distribution converges to a Normal (or Gaussian) distribution. StandardDeviation Mean

  13. Coin Flipping in Manhattan 4 1 6 1 4 At each step, we flip a coin to decide which way to go. What is the probability of ending at the intersection ofAvenue i and Street (n-i) after n steps?

  14. Coin Flipping in Manhattan 4 1 6 1 4 At each step, we flip a coin to decide which way to go. What is the probability of ending at the intersection ofAvenue i and Street (n-i) after n steps?

  15. Coin Flipping in Manhattan 4 1 6 1 4 At each step, we flip a coin to decide which way to go. What is the probability of ending at the intersection ofAvenue i and Street (n-i) after n steps?

  16. Coin Flipping in Manhattan 4 1 6 1 4 At each step, we flip a coin to decide which way to go. What is the probability of ending at the intersection ofAvenue i and Street (n-i) after n steps?

  17. Coin Flipping in Manhattan 4 1 6 1 4 At each step, we flip a coin to decide which way to go. What is the probability of ending at the intersection ofAvenue i and Street (n-i) after n steps?

  18. Coin Flipping in Manhattan 4 1 6 1 4 2n different paths to level n, each equally likely. The probability of i heads occurring on the path we generate is:

  19. n-step Random Walk on a line 4 1 6 1 4 Start at the origin: at each point, flip an unbiased coin to decide whether to go right or left. The probability that, in n steps, we take i steps to the right and n-i to the left (so we are at position 2i-n) is:

  20. n-step Random Walk on a line 4 1 6 1 4 Start at the origin: at each point, flip an unbiased coin to decide whether to go right or left. The probability that, in n steps, we take i steps to the right and n-i to the left (so we are at position 2i-n) is:

  21. n-step Random Walk on a line 4 1 6 1 4 Start at the origin: at each point, flip an unbiased coin to decide whether to go right or left. The probability that, in n steps, we take i steps to the right and n-i to the left (so we are at position 2i-n) is:

  22. n-step Random Walk on a line 4 1 6 1 4 Start at the origin: at each point, flip an unbiased coin to decide whether to go right or left. The probability that, in n steps, we take i steps to the right and n-i to the left (so we are at position 2i-n) is:

  23. n-step Random Walk on a line 4 1 6 1 4 Start at the origin: at each point, flip an unbiased coin to decide whether to go right or left. The probability that, in n steps, we take i steps to the right and n-i to the left (so we are at position 2i-n) is:

  24. = # of X with property Y Probability of X with property Y # of X Probabilities and counting • Say we want to count • the number of X's with property Y • One way to do it is to ask • "if we pick an X at random, what is the probability it has property Y?" • and then multiply by the number of X's.

  25. How many n-bit strings have an even number of 1’s? • If you flip a coin n times, what is the probability you get an even number of heads? Then multiply by 2n. • Say prob was q after n-1 flips. • Then, after n flips it is ½q + ½(1-q) = ½.

  26. Binomial distribution with bias p p 1-p 4 1 6 1 4 Start at the top. At each step, flip a coin with a bias p of heads to decide which way to go. What is the probability of ending at the intersection ofAvenue i and Street (n-i) after n steps?

  27. Binomial distribution with bias p p 1-p 4 1 6 1 4 Start at the top. At each step, flip a coin with a bias p of heads to decide which way to go. What is the probability of ending at the intersection ofAvenue i and Street (n-i) after n steps?

  28. Binomial distribution with bias p p 1-p p 1-p p 4 1 6 1 4 Start at the top. At each step, flip a coin with a bias p of heads to decide which way to go. What is the probability of ending at the intersection ofAvenue i and Street (n-i) after n steps?

  29. Overall probability we get i heads is: Binomial distribution with bias p p 1-p p 1-p p 4 1 6 1 4 Start at the top. At each step, flip a coin with a bias p of heads to decide which way to go. The probability of any fixed path withi heads(n-i tails)being chosen is: pi (1-p)n-i

  30. Bias p coin flipped n times. Probability of exactly i heads is:

  31. How many n-trit strings have even number of 0’s? • If you flip a bias 1/3 coin n times, what is the probability qn you get an even number of heads? Then multiply by 3n. [Why is this right?] • Say probability was qn-1 after n-1 flips. • Then, qn = (2/3)qn-1 + (1/3)(1-qn-1). • And q0=1. • pn = qn – ½ • pn = 1/3 pn-1 and p0 = ½. • Rewrite as: qn – ½ = 1/3(qn-1- ½) • So, qn – ½ = (1/3)n ½. Final count = ½ + ½3n

  32. Some puzzles

  33. Teams A and B are equally good. In any one game, each is equally likely to win. What is most likely length of a “best of 7” series? Flip coins until either 4 heads or 4 tails. Is this more likely to take 6 or 7 flips?

  34. Actually, 6 and 7 are equally likely To reach either one, after 5 games, it must be 3 to 2. ½ chance it ends 4 to 2. ½ chance it doesn’t.

  35. Another view W L W L W L 4 4 5 5 1 1 5 5 10 10 6 6 6 6 15 15 20 7 7

  36. Silver and Gold One bag has two silver coins, another has two gold coins, and the third has one of each. One of the three bags is selected at random. Then one coin is selected at random from the two in the bag. It turns out to begold. What is the probability that the other coin is gold?

  37. X 3 choices of bag 2 ways to order bag contents 6 equally likely paths.

  38. X X X X Given you see a , 2/3 of remaining paths have a second gold.

  39. So, sometimes, probabilities can be counter-intuitive

  40. Language Of Probability • The formal language of probability is a very important tool in describing and analyzing probability distributions.

  41. Finite Probability Distribution • A (finite) probability distributionD is a finite set S of elements, where each element x2S has a positive real weight, proportion, or probabilityp(x). • The weights must satisfy:

  42. Finite Probability Distribution • A (finite) probability distributionD is a finite set S of elements, where each element x2S has a positive real weight, proportion, or probabilityp(x). • For notational convenience we will define D(x) = p(x). • S is often called the sample space.

  43. Sample space • A (finite) probability distributionD is a finite set S of elements, where each element x2S has a positive real weight, proportion, or probabilityp(x). S Sample space

  44. Probability • A (finite) probability distributionD is a finite set S of elements, where each element x2S has a positive real weight, proportion, or probabilityp(x). S x weight or probability of x D(x) = p(x) = 0.2

  45. Probability Distribution • A (finite) probability distributionD is a finite set S of elements, where each element x2S has a positive real weight, proportion, or probabilityp(x). S 0.13 0.17 0.1 0.11 0.2 0 0.1 0.06 weights must sum to 1 0.13

  46. Events • A (finite) probability distribution D is a finite set S of elements, where each element x2S has a positive real weight, proportion, or probability p(x). • Any set E ½ S is called an event. The probability of event E is

  47. Events • A (finite) probability distributionD is a finite set S of elements, where each element x2S has a positive real weight, proportion, or probabilityp(x). S Event E

  48. Events • A (finite) probability distributionD is a finite set S of elements, where each element x2S has a positive real weight, proportion, or probabilityp(x). S 0.17 0 0.1 PrD[E] = 0.4 0.13

  49. Uniform Distribution • A (finite) probability distribution D has a finite sample space S, with elements x2S having probability p(x). • If each element has equal probability, the distribution is said to be uniform.

  50. Uniform Distribution • A (finite) probability distribution D has a finite sample space S, with elements x2S having probability p(x). S 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 Each p(x) = 1/9.