Robert Weiss
@biostatprof
Statistician, Teacher, Parent, Photographer
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As any parent can tell you, after the final four comes the terrible twos.
The EM algorithm needs to be renamed.
WOW! Musk just revealed the real reason for buying Twitter is to bring the p-value discussions on here to a satisfying end
This aging children thing is great, but only up to a point.
Bayesian inference is exact and small sample. One _can_ use asymptotics/approximations but it's not required.
perhaps i'm missing something, but i'm not sure that this connects to the question?
Bayesian inference is exact and small sample. One _can_ use asymptotics/approximations but not required.
perhaps i'm missing something, but i'm not sure that this connects to the question?
p( \theta | data ) \propto p( data | \theta) p( \theta) Don't need log concavity.
not sure whether this exists, but seems moderately likely: is there some { book / paper / ... } which demonstrates that under the assumption of a uniformly log-concave likelihood, one can do theory for statistical estimation in a more-or-less elementary, non-asymptotic fashion?
Covid's motto is "All for one and one for all." Going to an indoor meeting? No masks? No ventilation? If one of us has covid, we can all have covid. Share and share alike.
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