我在Python2.7中计算似然比检验时遇到问题。我有两个模型和相应的似然值。我认为比较模型L2是否优于模型L1(如果模型密切相关)的规则是查看-2*log(L2/L1)。然后我想找到对应于-2*log(L2/L1)的p值，并将其与L2优于L1的重要性相关联。这是我目前所拥有的:importnumpyasnpfromscipy.statsimportchisqprobL1=467400.#log(likelihood)ofmy1stfitL2=467414.#log(likelihood)ofmy2ndfitLR=-2.*np.log(L2/L1)#LR=-5.9905e-05p=ch

通过阅读sqlite文档，我发现了以下函数:http://www.sqlite.org/lang_corefunc.html#likelihoodThelikelihood(X,Y)functionreturnsargumentXunchanged.ThevalueYinlikelihood(X,Y)mustbeafloatingpointconstantbetween0.0and1.0,inclusive.Thelikelihood(X)functionisano-opthatthecodegeneratoroptimizesawaysothatitconsumesnoCPUcycl

通过阅读sqlite文档，我发现了以下函数:http://www.sqlite.org/lang_corefunc.html#likelihoodThelikelihood(X,Y)functionreturnsargumentXunchanged.ThevalueYinlikelihood(X,Y)mustbeafloatingpointconstantbetween0.0and1.0,inclusive.Thelikelihood(X)functionisano-opthatthecodegeneratoroptimizesawaysothatitconsumesnoCPUcycl

从LinearRegression到LogisticRegression给定二维样本数据集\(D=\left\{(\vec{x}_{1},y_{1}),(\vec{x}_{2},y_{2}),\ldots,(\vec{x}_{n},y_{n})\right\}\)，其中\(\vec{x}_{1},\ldots,\vec{x}_{n}\inX\)为\(d\)维向量（即\(X\)的size为\(n\timesd\)）,\(y_{1},\ldots,y_{n}\inY\)。我们希望得到一条直线\(Y=X\beta+\varepsilon\)来刻画\(X\)和\(Y\)之间的一般关系，由于真实数据集存