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Diff for: ‎notebooks/01a-instructor-probability-simulation.ipynb

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@@ -81,7 +81,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* Let's say that a website has a CTR of 50%, i.e. that 50% of people click through. If we picked 1000 people at random from thepopulation, how likely would it be to find that a certain number of people click?\n",
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"* Let's say that a website has a CTR of 50%, i.e. that 50% of people click through. If we picked 1000 people at random from the population, how likely would it be to find that a certain number of people click?\n",
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"\n",
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"We can simulate this using `numpy`'s random number generator.\n",
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"\n",
@@ -755,8 +755,6 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"___\n",
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"\n",
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"In the above, we saw that we could match data-generating processes with binary outcomes to the story of the binomial distribution.\n",
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"\n",
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"> The Binomial distribution's story is as follows: the number $r$ of successes in $n$ Bernoulli trials with probability $p$ of success, is Binomially distributed. \n",
@@ -1225,9 +1223,9 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "bayesian-modelling-tutorial",
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"language": "python",
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"name": "python3"
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"name": "bayesian-modelling-tutorial"
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},
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"language_info": {
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"codemirror_mode": {
@@ -1239,7 +1237,7 @@
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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": "3.7.2"
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"version": "3.7.3"
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},
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"toc-autonumbering": true
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},

Diff for: ‎notebooks/01a-student-probability-simulation.ipynb

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Diff for: ‎notebooks/01b-instructor-joint-conditional-probability.ipynb

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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "bayesian-modelling-tutorial",
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"language": "python",
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"name": "python3"
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"name": "bayesian-modelling-tutorial"
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},
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"language_info": {
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"codemirror_mode": {
@@ -566,7 +566,7 @@
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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": "3.7.2"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,

Diff for: ‎notebooks/01b-student-joint-conditional-probability.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -83,29 +83,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.2456\n"
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]
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{
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"data": {
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99-
"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Solution: Calculate P(A,B)\n",
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"x_0 = np.random.binomial(2, 0.5, 10000)\n",
@@ -118,20 +98,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.2523537"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Solution: Calculate P(A)P(B)\n",
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"x_1 = np.random.binomial(1, 0.5, 10000)\n",
@@ -167,20 +136,9 @@
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.724891534007516"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import data & store lengths in a pandas series\n",
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"df_12 = pd.read_csv('../data/finch_beaks_2012.csv')\n",
@@ -201,20 +159,9 @@
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.7239874466"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calculate P(A)P(B) using resampling methods\n",
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"n_samples = 100000\n",
@@ -232,20 +179,9 @@
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.7242"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calculate P(A,B) using resampling methods\n",
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"n_samples = 100000\n",
@@ -294,41 +230,19 @@
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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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"metadata": {},
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"outputs": [
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{
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"data": {
302-
"text/plain": [
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"0.8514056224899599"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Q1 Answer\n",
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"___"
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]
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.6942148760330579"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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