Created using Colaboratory

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udlbook
2022-10-25 11:44:09 +01:00
parent 3ed9864417
commit 015a34e3d4

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@@ -5,7 +5,7 @@
"colab": {
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyPBGTxs29MOs80wv5LpyIcV",
"authorship_tag": "ABX9TyOE0qm8aIpEaNh/aI9gnlkY",
"include_colab_link": true
},
"kernelspec": {
@@ -244,24 +244,6 @@
"id": "R5z_0dzQMF35"
}
},
{
"cell_type": "code",
"source": [
"# Return the likelihood of all of the data under the model\n",
"def compute_likelihood(y_train, lambda_param):\n",
" # TODO -- compute the likelihood of the data -- the product of the normal probabilities for each data point\n",
" # Top line of equation 5.3 in the notes\n",
" # You will need np.prod() and the normal_distribution function you used above\n",
" # Replace the line below\n",
" likelihood = 0\n",
" return likelihood"
],
"metadata": {
"id": "zpS7o6liCx7f"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
@@ -339,6 +321,24 @@
"id": "OgcRojvPWh4V"
}
},
{
"cell_type": "code",
"source": [
"# Return the likelihood of all of the data under the model\n",
"def compute_likelihood(y_train, lambda_param):\n",
" # TODO -- compute the likelihood of the data -- the product of the Bernoulli probabilities for each data point\n",
" # Top line of equation 5.3 in the notes\n",
" # You will need np.prod() and the bernoulli_distribution function you used above\n",
" # Replace the line below\n",
" likelihood = 0\n",
" return likelihood"
],
"metadata": {
"id": "zpS7o6liCx7f"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [