Perhaps it's shoddy coding on my part, but I got different result as well. It was actually raining for 111801 days out of a 1000000 Albert and Charlie said yes 81833 Betty and Charlie said yes 82640 Albert and Charlie said yes and it was true 49250 Betty and Charlie said yes and it was true 49631 Albert and Charlie said yes and it was a lie 16300 Betty and Charlie said yes and it was a lie 16681 It wasn't raining but everyone lied 32658 Times said 'yes' truthfully: 74377 Times said 'no' truthfully: 592897 Times said 'yes' and lied: 295302 Times said 'no' and lied: 37424 Betty Times said 'yes' truthfully: 74422 Times said 'no' truthfully: 592248 Times said 'yes' and lied: 295951 Times said 'no' and lied: 37379 Charlie Times said 'yes' truthfully: 74326 Times said 'no' truthfully: 592276 Times said 'yes' and lied: 295923 Times said 'no' and lied: 37475 I have used probabilities from the article. One out of three chance for a lie, one out of nine chance of rain being in Seattle. I can share my code if you would like to go for "maybe by spotting a problem with someone else's code I'll get some extra insight" type of exercise. Beware of sleepy Python though ;). Sample size for simulation: 1000000 In following the third person was saying the opposite: Albert and Betty said yes 82200 Albert and Betty said yes and it was true 49596 Albert and Betty said yes and it was a lie 16646 It was raining and everyone said true 32950 Albert
They say it rains about 10% of the time, and "A base rate of 10% corresponds to prior odds of 1:9." I found this notation a bit confusing, but I think it corresponds to a one-in-ten chance of rain. For each rainy day, you get nine sunny days. The difference between probabilities expressed as a value between 0 and 1 and these "odds" if that's what 1:9 is called is likely part of my confusion in following the article. It was actually Professor Brian who demonstrated how useful a simulation can be, while analyzing this bizarre problem: But a simulator, based on a random number generator, seems like a good way to check our work. I still trust my numbers as long as I feel like I know what I am doing. Say we start with a convenient number of days: 243 sunny 27 rainy Each friend will be expected to give the same ratio of answers. We call Albert first and he responds: False "yes" on 1/3 of 243 sunny days = 81 days True "yes" on 2/3 of 27 rainy days = 18 days False "no" on 1/3 of 27 rainy days = 9 days So Albert says "yes" on 99 days, and on 81 days it is sunny and on 18 days it is raining. We increase our expectation of rain from 10% to 18/99, about 18%. We expect the same ratio of responses from Betty: False "yes" on 1/3 of 81 sunny days = 27 days True "yes" on 2/3 of 18 rainy days = 12 days False "no" on 1/3 of 18 rainy days = 6 days So Betty says "yes" on 39 days, and on 27 days it is sunny and on 12 days it is raining. We increase our expectation of rain from 18/99 to 12/39, about 31%. We expect the same ratio of responses from Charlie: False "yes" on 1/3 of 27 sunny days = 9 days True "yes" on 2/3 of 12 rainy days = 8 days False "no" on 1/3 of 12 rainy days = 4 days So Charlie says "yes" on 17 days, and on 9 days it is sunny and on 8 days it is raining. We increase our expectation of rain from 12/39 to 8/17, about 47%. This seems very clear and agrees with the given answer. But I still haven't used Bayes' theorem.one out of nine chance of rain being in Seattle
Say you know a family has two children, and further that at least one of them is a girl named Florida. What is the probability that they have two girls?
270 days True "no" on 2/3 of 243 sunny days = 162 days True "no" on 2/3 of 81 sunny days = 54 days True "no" on 2/3 of 27 sunny days = 18 days
Using odds instead of percentage makes this problem simple. Our estimate of rain in Seattle is 1:9 (one rainy day for every nine sunny days). Our friends tell us the truth with odds of 2:1 (two truthful reports for each false report). So after calling one friend and hearing "yes it's raining" we multiply 1:9 by 2:1 and get odds of 2:9 as our new expectation of rain. That's 2/11, about 18%, so we are more confident of rain. The second friend says "yes" and we multiply 2:9 by another 2:1 to get 4:9, or 4/13 which is about 31%. The third friend says "yes" and we multiply 4:9 by 2:1 to get 8:9 which is 8/17, the final answer of about 47%. I have found a way to force Bayes in, but don't know if it's correct. Replacing the A's and B's with more descriptive terms, Bayes' Theorem is odds of rain, given a "yes" = odds of "yes," given rain × odds of rain / odds of "yes" So, after hearing the first "yes" we have odds of rain, given a "yes" = (2:1 the odds we will hear a "yes" on a rainy day) × (1:9 our prior estimate for rain) / (1, our certainty of hearing "yes" because we just talked with Albert and he definitely said "yes") This gives us 2:9 divided by 1, which is the desired 2/11 result. That denominator seems forced and flaky, though, and I am not sure what to do if Albert says "no." Perhaps the odds of rain given a "no" are 1:2 × 1:9 / 1 again because we definitely heard "no" for a total of 1:18 or about 5% chance of rain. Odds are good b_b will be able to straighten this out.