Jan
25
awarded Student
Jan
24
asked Black-Litterman proof with P=I and Omega=tau*Sigma
Jan
15
awarded Student
Jan
15
awarded Editor
Jan
15
revised MATLAB GPU-enabled function?
To try and remain on-topic. More specific about whether knowing what I'm doing is GPU-enabled.
Jan
15
asked MATLAB GPU-enabled function?
2018
Dec
6
comment Naive Bayes where posterior almost always equals prior (in MATLAB)
Dave, I edited the question to suggest I think this was the solution. Thank you. Even so, I realize I am unclear as to how categorical data is interpreted in naive Bayes: you discuss the 256 partitions, with (I think) a = 2 (classifying, say, "yes" or "no") and m=n=p=q=2 if we assume only 2 unique attributes within each category. I thought naive Bayes would simply assign a probability to each variable within each category and would ignore a variable to be classified if there was no training example. Can you clear this up or point me toward a good reference?
Dec
6
revised Naive Bayes where posterior almost always equals prior (in MATLAB)
Added "Edit" section to follow-up from Dave's questions.
Dec
6
asked Naive Bayes where posterior almost always equals prior (in MATLAB)
Oct
22
comment Gauss' proof that "the best estimate for a random variable is the average"
I think this is correct, @whuber. If you paste your comment as an answer I can select it. In the meantime, I ordered Stewart's 1987 translation of the Gauss work you mention; I will post page numbers and confirm the answer once I make my way through it. Thanks to all.
Oct
20
comment Gauss' proof that "the best estimate for a random variable is the average"
Thank you, @IrishStat. I will see how this question develops elsewhere before selecting a solution, but regardless this is a very helpful link. Thank you!
Oct
20
comment Gauss' proof that "the best estimate for a random variable is the average"
Thanks, @whuber, for the helpful clarification. I believe the original (quoted) author's intent was the former option; i.e., in the instance in which there is no sample data, and "average" is meant as "expectation." As for "best," I'm sure that would be outlined in the actual proof, but I think what the original (quoted) author meant was that this result should (at least) not be inferior to any other formulation. Again, thanks.
Oct
20
asked Gauss' proof that "the best estimate for a random variable is the average"
Sep
26
awarded Editor
Sep
26
revised Statistical significance from an iterated count
grammar, exposition.
Sep
26
awarded Informed
Sep
26
awarded Student
Sep
26
asked Statistical significance from an iterated count