September 2014
Intermediate to advanced
236 pages
4h 17m
Japanese
Content preview from Think Bayes ―プログラマのためのベイズ統計入門
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200
■
15
章
次元を扱う
サンプル数
図 15-5 シミュレーションした検体B1242 の希薄化曲線
15.11
ジョイント事後確率
これらのシミュレーションを使って、
num_new
と
k
のジョイント分布を推定して、
k
の
任意の値を条件とする
num_new
の分布を求めることができる。
def MakeJointPredictive(curves):
joint = thinkbayes.Joint()
for curve in curves:
for k, num_new in curve:
joint.Incr((k, num_new))
joint.Normalize()
return joint
MakeJointPredictive
は、値がタプル（
tuple
）の
Pmf
であるジョイントオブジェクトを
作る。
curves
は、
RunSimulation
で作られた希薄化曲線のリストである。各曲線には、
k
と
num_new
の対のリストが含まれる。
15.11
ジョイント事後確率
■
201
結果として得られるジョイント分布は、各対から生起確率への対応となる。ジョイ
ント分布があれば、
Joint.Conditional
を使って、
k
を条件とした
num_new
の分布を得
る（「9.6 条件付き分布」参照） ...
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