Exact Approximate Bayesian computation :) Thanks!

]]>Yes, this is “exact ABC” since you wait for the data and pseudo-data to be equal.

]]>Can I ask, since I use an “exact rejection sampling” algorithm in the socks blog post (that is, I sort of use the “identity function” as the summary statistics) is it still ok to call it approximate bayesian computation? (which I do call it…) It is still likelihood free, at least.

]]>The socks were fun! ABC is one of those techniques that I really wished that somebody told me about earlier. Just like I wished somebody had told me about bootstrap before I had to go through the t-test/chi-square/anova dance that is psychology statistics. Ah, the missing ones! Would be difficult to estimate without extensive data regarding a persons geographical location over time. Would it be easier if the socks were missing at random? :)

]]>I still have to write the slides for the Bayesian section so I may follow your advice. Good job with the socks, by the way. Could you extend to the problem of finding the missing ones?!

]]>I’m looking forward to the next lectures in this course.

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