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Nov
21
comment What algorithm is used by computers to calculate logarithms?
@Aryabhata: Thank you for the updated links. I'm looking forward to reading the material.
Nov
17
comment What algorithm is used by computers to calculate logarithms?
@Aryabhata: If I google "What algorithm is used by computers to calculate logarithms", then the first answer shown is this StackExchange webpage, and the first answer is yours with the broken links. That is not very useful.
Nov
15
comment What algorithm is used by computers to calculate logarithms?
@Aryabhata: The links in your post are no longer active. Regarding your statement "this pdf you can find by a google search of the title," what title are you referring to? I would like to search for whatever that is.
Oct
17
accepted Probability of server cluster failure
Oct
14
awarded  Supporter
Oct
14
comment Probability of server cluster failure
Thank you. Now that I look further, I find it kind of extraordinary that $Pr$(0 servers fail) = $0.00592$. That seems really low when each of the servers seems pretty stable with each having probability $0.95$ of failing. But the math proves it, I guess.
Oct
14
comment Probability of server cluster failure
Thanks. So when I was earlier computing that $Pr$(2 server fails) = $0.95^{98}×0.05^2$, does that imply that I was computing the probability that the first 98 servers were good and the last 2 servers failed?
Oct
14
asked Probability of server cluster failure
Aug
21
awarded  Nice Question
Jul
2
awarded  Curious
Nov
16
asked How do I state a reduction in cost?
Sep
18
awarded  Commentator
Sep
18
comment Convex hull questions
Thanks. Can you point me to any resources on how to create a line or hyperplane through the "B" point that does not pass through any "A" point?
Sep
17
revised Convex hull questions
added 1162 characters in body
Sep
17
asked Convex hull questions
Sep
15
awarded  Notable Question
Sep
2
comment Why would I use Bayes' Theorem if I can directly compute the posterior probability?
Regarding (3) and my example with 10 years of data, would it be ok to compute a prior probability for $P(team\ wins)$ directly from the winning percentage over 9 seasons, and then use the data from the 10th season to compute the likelihood $P(team\ scores\ 100 | team\ wins)$ and evidence $P(team\ scores\ 100)$, with the resulting posterior probability distribution being used to predict games in the future (after the 10th season is over)? Why not just use all 10 seasons to compute the prior, likelihood, and evidence?
Sep
2
accepted Why would I use Bayes' Theorem if I can directly compute the posterior probability?
Sep
1
comment Why would I use Bayes' Theorem if I can directly compute the posterior probability?
Thanks for the detailed answer. Follow-up questions: (1) If I compute $P(team\ wins | team\ scores\ 100)$ directly as I suggested, would it be correctly called a 'conditional probability' rather than a 'posterior probability'? (2) Is the prior probability the ONLY difference between a frequentist's conditional probability and a Bayesian's posterior probability? That is, the Bayesian pulls the prior out of somewhere, but not from the sample? (3) Where would I get the prior if not from the sample? Would I compute it from 10 seasons of NBA games rather than a sample of one season?
Aug
26
comment Why would I use Bayes' Theorem if I can directly compute the posterior probability?
Can you explain the difference between probability and density? By "density", do you mean "distribution", like a Gaussian distribution? By the way, I'm a software programmer, not a mathematician. Also, how can you safely say that "You don't know $f(\theta|x)$ generally, but you know $f(x|\theta)$ ..."? In my example with the logs of all NBA games, I would be able to compute $f(\theta|x)$, right?