Update 1.Introduction_to_statistical_learning_methods.ipynb

第01章 统计学习方法概论/1.Introduction_to_statistical_learning_methods.ipynb

@@ -406,7 +406,7 @@
     "**伯努利模型的极大似然估计：**  \n",
     "定义$P(Y=1)$概率为$p$，可得似然函数为：$$L(p)=f_D(y_1,y_2,\\cdots,y_n|\\theta)=\\binom{n}{k}p^k(1-p)^{(n-k)}$$方程两边同时对$p$求导，则：$$\\begin{aligned}\n",
     "0 & = \\binom{n}{k}[kp^{k-1}(1-p)^{(n-k)}-(n-k)p^k(1-p)^{(n-k-1)}]\\\\\n",
-    "& = \\binom{n}{k}[p^{(k-1)}(1-p)^{(n-k-1)}(m-kp)]\n",
+    "& = \\binom{n}{k}[p^{(k-1)}(1-p)^{(n-k-1)}(k-np)]\n",
     "\\end{aligned}$$可解出$p$的值为$p=0,p=1,p=k/n$，显然$\\displaystyle P(Y=1)=p=\\frac{k}{n}$  \n",
     "\n",
     "**伯努利模型的贝叶斯估计：**  \n",