Timeline for Detect rare high-value measurements in a series of measurements
Current License: CC BY-SA 4.0
8 events
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| Apr 23 at 16:05 | comment | added | Basj | Just to be sure @Eoin, "Doing Bayesian Data Analysis" is not in free access, it is a paid book, is that correct? If so, I'll see if they have it in my local university library. In the meantime, do you have an introduction reference to the method/approach you're using here, with some details? (maybe Wikipedia ?) | |
| Apr 22 at 14:29 | comment | added | Eoin |
The Dirchlet distribution is a common prior distribution for parameters that represent proportions (all of the values of theta have to add up to 1). I've used a very broad prior here, so this will have little impact on your parameter estimates.
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| Apr 22 at 14:27 | comment | added | Eoin |
Theta a pair of parameters indicating what proportion of the data comes from the first distribution (Theta[1]) and what proportion comes from the second (Theta[2]).
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| Apr 22 at 14:23 | comment | added | Basj |
Yes @Eoin, I don't have the background, but I'll try to read about it. What does Theta (and Theta[2]) represent, and why a Dirichlet distribution?
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| Apr 22 at 14:17 | comment | added | Eoin |
Sorry, yes, this won't totally make sense if you're not familiar with Bayesian methods in general. I'd suggest Doing Bayesian Data Analysis as a great introduction to the topic. You can see from the table that Theta[2] has an estimated value of 0.5, and a 95% confidence interval of [0.3, 0.7].
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| Apr 22 at 13:02 | comment | added | Basj |
Thank you for your answer. I am not familiar with this approach. What is the conclusion after the final histogram of posterior_one_minus_pi? "The maximum likelihood is reached when $1-\pi = 0.05$", or is there another way to write the conclusion of this approach?
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| Apr 22 at 13:00 | history | edited | Eoin | CC BY-SA 4.0 |
added 184 characters in body
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| Apr 22 at 12:50 | history | answered | Eoin | CC BY-SA 4.0 |