Presentation on theme: "Physics 114: Lecture 9 Probability Density Functions Dale E. Gary NJIT Physics Department."— Presentation transcript:
Physics 114: Lecture 9 Probability Density Functions Dale E. Gary NJIT Physics Department
February 12, 2010 Binomial Distribution If you raise the sum of two variables to a power, you get: Writing only the coefficients, you begin to see a pattern:
February 12, 2010 Binomial Distribution Remarkably, this pattern is also the one that governs the possibilities of tossing n coins : With 3 coins, there are 8 ways for them to land, as shown above. In general, there are 2 n possible ways for n coins to land. How many permutations are there for a given row, above, e.g. how many permutations for getting 1 head and 2 tails? Obviously, 3. How many permutation for x heads and n x tails, for general n and x ? n 2 n Number of combinations in each row: (n choose x)
February 12, 2010 Probability With fair coins, tossing a coin will result in equal chance of 50%, or ½, of its ending up heads. Let us call this probability p. Obviously, the probability of tossing a tails, q, is q = (1 p). With 3 coins, the probability of getting any single one of the combinations is 1/2 n = 1/8 th, (since there are 8 combinations, and each is equally probable). This comes from (½) (½) (½), or the product of each probability p = ½ to get a heads. If we want to know the probability of getting, say 1 heads and 2 tails, we just need to multiply the probability of any combination (1/8 th ) by the number of ways of getting 1 heads and 2 tails, i.e. 3, for a total probability of 3/8. To be really general, say the coins were not fair, so p ≠ q. Then the probability to get heads, tails, tails would be (p)(q)(q) = p 1 q 2. Finally the probability P(x; n, p) of getting x heads given n coins each of which has probability p, is With 3 coins, there are 8 ways for them to land, as shown above. In general, there are 2 n possible ways for n coins to land. How many permutations are there for a given row, above, e.g. how many permutations for getting 1 head and 2 tails? Obviously, 3. How many permutation for x heads and n x tails, for general n and x ?
February 12, 2010 Binomial Distribution This is the binomial distribution, which we write P B : Let’s see if it works. For 1 heads with a toss of 3 fair coins, x = 1, n = 3, p = ½, we get For no heads, and all tails, we get Say the coins are not fair, but p = ¼. Then the probability of 2 heads and 1 tails is: You’ll show for homework that the sum of all probabilities for this (and any) case is 1, i.e. the probabilities are normalized. Note: 0! 1
February 12, 2010 Binomial Distribution To see the connection of this to the sum of two variables raised to a power, replace a and b with p and q : Since p + q = 1, each of these powers also equals one on the left side, while the right side expresses how the probabilities are split among the different combinations. When p = q = ½, for example, the binomial triangle becomes In MatLAB, use binopdf(x,n,p) to calculate one row of this triangle, e.g. binopdf(0:3,3,0.5) prints 0.125, 0.375, 0.375, / 2 1 / 2 1 / 4 2 / 4 1 / 4 1 / 8 3 / 8 3 / 8 1 / 8 1 / 16 4 / 16 6 / 16 4 / 16 1 / 16
February 12, 2010 Binomial Distribution Let’s say we toss 10 coins, and ask how many heads we will see. The 10 th row of the triangle would be plotted as at right. The binomial distribution applies to yes/no cases, i.e. cases where you want to know the probability of something happening, vs. it not happening. Say we want to know the probability of getting a 1, rolling five 6-sided dice. Then p = 1/6 (the probability of rolling a 1 on one die), and q = 1 – p = 5/6 (the probability of NOT rolling a 1). The binomial distribution applies to this case, with P B (x,5,1/6). The plot is shown at right. >> binopdf(0:5,5,1/6.) ans =
February 12, 2010 Binomial Distribution Mean Let’s say we toss 10 coins N = 100 times. Then we would multiple the PDF by N, to find out how many times we would have x number of heads. The mean of the distribution is, as before: For 10 coins, with p = ½, we get = np = 5. For 5 dice, with p = 1/6, we get = np = 5/
February 12, 2010 Binomial Standard Deviation The standard deviation of the distribution is the “second moment,” given by the variance: For 10 coins, with p = ½, we get For 5 dice, with p = 1/6, we get
February 12, 2010 Summary of Binomial Distribution The binomial distribution is P B : The mean is The standard deviation is
February 12, 2010 Poisson Distribution An approximation to the binomial distribution is very useful for the case where n is very large (i.e. rolls with a die with infinite number of sides?) and p is very small—called the Poisson distribution. This is the case of counting experiments, such as the decay of radioactive material, or measuring photons in low light level. To derive it, start with the binomial distribution with n large and p << 1, but with a well defined mean = np. Then The term because x is small, so most of the terms cancel leaving a total of x terms each approximately equal to n. This gives
February 12, 2010 Poisson Distribution Now, the term (1 – p) x 1, for small p, and with some algebra we can show that the term (1 – p) n e . Thus, the final Poisson distribution depends only on x and , and is defined as The text shows that the expectation value of x (i.e. the mean) is Remarkably, the standard deviation is given by the second moment as These are a little tedious to prove, but all we need for now is to know that the standard deviation is the square-root of the mean.
February 12, 2010 Example 2.3 Some students measure some background counts of cosmic rays. They recorded numbers of counts in their detector for a series of s intervals, and found a mean of 1.69 counts/interval. They can use the standard deviation formula from chapter 1, which is to get a standard deviation directly from the data. They do this and get s = They can also estimate the standard deviation by Now they change the length of time they count from 2-s intervals to 15-s intervals. Now the mean number of counts in each interval will increase. Now they measure a mean of 11.48, which implies while they again calculate s directly from their measurements to find s = We can plot the theoretical distributions using MatLAB poisspdf(x,mu), e.g. poisspdf(0:8,1.69) gives ans =
February 12, 2010 Example 2.3, cont’d The plots of the distributions is shown for these two cases in the plots at right. You can see that for a small mean, the distribution is quite asymmetrical. As the mean increases, the distribution becomes somewhat more symmetrical (but is still not symmetrical at counts/interval). I have overplotted the mean and standard deviation. You can see that the mean does not coincide with the peak (the most probable value).
February 12, 2010 Example 2.3, cont’d Here is the higher-mean plot with the equivalent Gaussian (normal distribution) overlaid. For large means (high counts), the Poisson distribution approaches the Gaussian distribution, which we will describe further next time.