Likelihood should actually be between 0 and 1 as claimed

Author: radford

Chapter5_LossFunctions/LossFunctions.ipynb

@@ -73,7 +73,7 @@
       "- $ L( \\theta, \\hat{\\theta} ) = \\frac{ | \\theta - \\hat{\\theta} | }{ \\theta(1-\\theta) }, \\; \\; \\hat{\\theta}, \\theta \\in [0,1] $ emphasizes an estimate closer to 0 or 1 since if the true value $\\theta$ is near 0 or 1, the loss will be *very* large unless $\\hat{\\theta}$ is similarly close to 0 or 1. \n",
       "This loss function might be used by a political pundit who's job requires him or her to give confident \"Yes/No\" answers. This loss reflects that if the true parameter is close to 1 (for example, if a political outcome is very likely to occcur), he or she would want to strongly agree as to not look like a skeptic. \n",
       "\n",
-      "-  $L( \\theta, \\hat{\\theta} ) =  1 - \\exp \\left( (\\theta -  \\hat{\\theta} )^2 \\right) $ is bounded between 0 and 1 and reflects that the user is indifferent to suffiently-far-away estimates. It is similar to the zero-one loss above, but not quite as penalizing to estimates that are close to the true parameter. \n",
+      "-  $L( \\theta, \\hat{\\theta} ) =  1 - \\exp \\left( -(\\theta -  \\hat{\\theta} )^2 \\right) $ is bounded between 0 and 1 and reflects that the user is indifferent to suffiently-far-away estimates. It is similar to the zero-one loss above, but not quite as penalizing to estimates that are close to the true parameter. \n",
       "-  Complicated non-linear loss functions can programmed: \n",
       "\n",
       "        def loss(true_value, estimate):\n",