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‎Interactive_Bayesian_Updating.ipynb‎

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@@ -102,12 +102,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"supress_warnings = False\n",
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"supress_warnings = True\n",
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"from ipywidgets import interactive # widgets and interactivity\n",
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"from ipywidgets import widgets # widgets and interactivity\n",
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"import matplotlib; import matplotlib.pyplot as plt # plotting\n",
@@ -140,7 +140,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -177,7 +177,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -198,18 +198,18 @@
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"\n",
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"def run_plot_summary1(P_happening,P_positive_given_happening,P_positive_given_not_happening):\n",
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" digits = 3\n",
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" P_not_positive_given_happening = np.round((1 - P_positive_given_happening),digits)\n",
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" P_not_positive_given_not_happening = np.round((1 - P_positive_given_not_happening),digits)\n",
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" P_not_happening = np.round((1.0 - P_happening),digits)\n",
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" P_not_positive_given_happening = (1 - P_positive_given_happening)\n",
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" P_not_positive_given_not_happening = (1 - P_positive_given_not_happening)\n",
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" P_not_happening = (1.0 - P_happening)\n",
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" \n",
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" P_positive = np.round((P_positive_given_happening * P_happening + P_positive_given_not_happening * P_not_happening),digits)\n",
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" P_not_positive = np.round((P_not_positive_given_happening * P_happening + P_not_positive_given_not_happening * P_not_happening),digits)\n",
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" P_positive = (P_positive_given_happening * P_happening + P_positive_given_not_happening * P_not_happening)\n",
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" P_not_positive = (P_not_positive_given_happening * P_happening + P_not_positive_given_not_happening * P_not_happening)\n",
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" \n",
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" P_happening_given_positive = np.round(((P_positive_given_happening * P_happening) / P_positive),digits)\n",
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" P_not_happening_given_positive = np.round((P_positive_given_not_happening * P_not_happening) / P_positive,digits)\n",
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" P_happening_given_positive = ((P_positive_given_happening * P_happening) / P_positive)\n",
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" P_not_happening_given_positive = ((P_positive_given_not_happening * P_not_happening) / P_positive)\n",
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" \n",
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" P_happening_given_not_positive = np.round(((P_not_positive_given_happening * P_happening) / P_not_positive),digits)\n",
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" P_not_happening_given_not_positive = np.round(((P_not_positive_given_not_happening * P_not_happening) / P_not_positive),digits)\n",
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" P_happening_given_not_positive = ((P_not_positive_given_happening * P_happening) / P_not_positive)\n",
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" P_not_happening_given_not_positive = ((P_not_positive_given_not_happening * P_not_happening) / P_not_positive)\n",
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" \n",
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" plt.subplot(111)\n",
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" plt.plot()\n",
@@ -283,13 +283,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 42,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9f463f05380247aca636afc2264ae0a0",
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"model_id": "081c8551585642b2b2ea6136696b799d",
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"version_major": 2,
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"version_minor": 0
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},
@@ -303,12 +303,12 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "70ad5e2b4fc24f499708c4b904c1dc3a",
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"model_id": "1d10d27396dd478e8c684f41495c3fd9",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Output()"
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"Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 1 Axes>', 'i…"
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]
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},
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"metadata": {},
@@ -343,7 +343,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -362,18 +362,18 @@
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"\n",
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"def run_plot_summary(P_happening,P_positive_given_happening,P_positive_given_not_happening):\n",
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" digits = 3\n",
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" P_not_positive_given_happening = np.round((1 - P_positive_given_happening),digits)\n",
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" P_not_positive_given_not_happening = np.round((1 - P_positive_given_not_happening),digits)\n",
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" P_not_happening = np.round((1.0 - P_happening),digits)\n",
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" P_not_positive_given_happening = (1 - P_positive_given_happening)\n",
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" P_not_positive_given_not_happening = (1 - P_positive_given_not_happening)\n",
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" P_not_happening = (1.0 - P_happening)\n",
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" \n",
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" P_positive = np.round((P_positive_given_happening * P_happening + P_positive_given_not_happening * P_not_happening),digits)\n",
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" P_not_positive = np.round((P_not_positive_given_happening * P_happening + P_not_positive_given_not_happening * P_not_happening),digits)\n",
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" P_positive = (P_positive_given_happening * P_happening + P_positive_given_not_happening * P_not_happening)\n",
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" P_not_positive = (P_not_positive_given_happening * P_happening + P_not_positive_given_not_happening * P_not_happening)\n",
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" \n",
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" P_happening_given_positive = np.round(((P_positive_given_happening * P_happening) / P_positive),digits)\n",
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" P_not_happening_given_positive = np.round((P_positive_given_not_happening * P_not_happening) / P_positive,digits)\n",
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" P_happening_given_positive = ((P_positive_given_happening * P_happening) / P_positive)\n",
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" P_not_happening_given_positive = (P_positive_given_not_happening * P_not_happening) / P_positive\n",
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" \n",
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" P_happening_given_not_positive = np.round(((P_not_positive_given_happening * P_happening) / P_not_positive),digits)\n",
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" P_not_happening_given_not_positive = np.round(((P_not_positive_given_not_happening * P_not_happening) / P_not_positive),digits)\n",
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" P_happening_given_not_positive = ((P_not_positive_given_happening * P_happening) / P_not_positive)\n",
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" P_not_happening_given_not_positive = ((P_not_positive_given_not_happening * P_not_happening) / P_not_positive)\n",
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" \n",
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" plt.subplot(211)\n",
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" plt.plot()\n",
@@ -531,13 +531,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 47,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "65fba05d2f634969b120b5c7d078538b",
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"model_id": "d72830d524704fe69cf2ef205e0ad4c5",
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"version_major": 2,
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"version_minor": 0
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6e48ff1254f1405b8ac2fbf7077f467f",
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"model_id": "a1990c0264884670a49ffa4ecf045daa",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Output()"
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"Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 2 Axes>', 'i…"
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]
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},
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"scrolled": false
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},
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"execution_count": 49,
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"metadata": {},
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"outputs": [],
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"source": [
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"l = widgets.Text(value=' Bayesian Updating Demo, Michael Pyrcz, Professor, The University of Texas at Austin',\n",
@@ -682,13 +680,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 52,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9a31e99091f84068a54e39d26bccbfca",
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"model_id": "b4f1ca08d731472986942298dc82a0a1",
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"version_major": 2,
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"version_minor": 0
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "858e1eda25dd4a0a8f70c9cdf3032709",
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"model_id": "2f3199f4b3d243b5932dd4d9a3571ced",
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"version_major": 2,
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"version_minor": 0
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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"version": "3.12.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

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