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Think Bayes ―プログラマのためのベイズ統計入門
book

Think Bayes ―プログラマのためのベイズ統計入門

by Allen B. Downey, 黒川 利明
September 2014
Intermediate to advanced
236 pages
4h 17m
Japanese
O'Reilly Japan, Inc.
Content preview from Think Bayes ―プログラマのためのベイズ統計入門
15.9
 おへそのデータ
193
ps = gammas[i-1] / sums
log_likes = numpy.log(ps) * count
log_likes -= numpy.max(log_likes)
likes = numpy.exp(log_likes)
return likes
これは
Species2.SampleLikelihood
と似ている。違いは、各更新に
1
つの種しか含
まれないので、ループが要らないことである。
この関数の実行時間は、仮説の個数
k
に比例する。これを
m
回実行するので、更新
の実行時間は
km
に比例する。正確な結果を得るために必要な反復回数は、普通は小
さくなる。
15.9
 おへそのデータ
ライオンとトラとクマについてはこれで十分だ。おへその問題に戻ろう。データがど
んなものかという感じをつかむために、検体
B1242
400
の読み込みで次のような個数
61
種が得られたものを見てみよう。
92, 53, 47, 38, 15, 14, 12, 10, 8, 7, 7, 5, 5,
4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
いくつかの支配的な種が、全体のかなりの部分を占めるが、多くの種は、
1
つしか読
み込まれていない。 ...
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Publisher Resources

ISBN: 9784873116945Other