Simulating posterior draws

You have just decided to use a Beta(5, 2) prior for the efficacy rate. You are also using the binomial distribution to model the data (curing a sick patient is a "success", remember?). Since the beta distribution is a conjugate prior for the binomial likelihood, you can simply simulate the posterior!

You know that if the prior is \(Beta(a, b)\), then the posterior is \(Beta(x, y)\), with:

\(x = NumberOfSuccesses + a\),

\(y = NumberOfObservations - NumberOfSuccesses + b\).

Can you simulate the posterior distribution? Recall that altogether you have data on 22 patients, 19 of whom have been cured. numpy and seaborn have been imported for you as np and sns, respectively.

This exercise is part of the course

Bayesian Data Analysis in Python

Exercise instructions

Assign the numbers of patients treated and cured to num_patients_treated and num_patients_cured, respectively.
Use the appropriate numpy function to sample from the posterior distribution and assign the result to posterior_draws.
Plot the posterior distribution using the appropriate seaborn function.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Define the number of patients treated and cured
num_patients_treated = ____
num_patients_cured = ____

# Simulate 10000 draws from the posterior distribuition
posterior_draws = ____(____ + ____, ____ - ____ + ____, 10000)

# Plot the posterior distribution
____(____, shade=True)
plt.show()

Edit and Run Code

This exercise is part of the course

Bayesian Data Analysis in Python

IntermediateSkill Level

4.8+

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Take your first steps in the Bayesian world. In this chapter, you’ll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, you’ll build your first Bayesian model to draw conclusions from randomized coin tosses.

Exercise 1: Who is Bayes? What is Bayes?Exercise 2: Bayesians vs. Frequentists Exercise 3: Probability distributions Exercise 4: Probability and Bayes' Theorem Exercise 5: Let's play cards Exercise 6: Bayesian spam filter Exercise 7: What does the test say?Exercise 8: Tasting the Bayes Exercise 9: Tossing a coin Exercise 10: The more you toss, the more you learn Exercise 11: Hey, is this coin fair?

It’s time to look under the Bayesian hood. You’ll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, you’ll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.

Exercise 1: Under the Bayesian hood Exercise 2: Towards grid approximation Exercise 3: Grid approximation without prior knowledge Exercise 4: Updating posterior belief Exercise 5: Prior belief Exercise 6: The truth of the prior Exercise 7: Picking the right prior Exercise 8: Simulating posterior draws

Current Exercise

Exercise 9: Reporting Bayesian results Exercise 10: Point estimates Exercise 11: Highest Posterior Density credible intervals Exercise 12: The meaning of credibility

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Exercise 1: A/B testing Exercise 2: Simulate beta posterior Exercise 3: Posterior click rates Exercise 4: A or B, and how sure are we?Exercise 5: How bad can it be?Exercise 6: Decision analysis Exercise 7: Decision analysis: cost Exercise 8: Decision analysis: profit Exercise 9: Regression and forecasting Exercise 10: Defining a Bayesian regression model Exercise 11: Analyzing regression parameters Exercise 12: Predictive distribution

In this final chapter, you’ll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, you’ll apply what you’ve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!

Exercise 1: Markov Chain Monte Carlo and model fitting Exercise 2: Markov Chain Monte Carlo Exercise 3: Sampling posterior draws Exercise 4: Interpreting results and comparing models Exercise 5: Inspecting posterior draws Exercise 6: Comparing models with WAIC Exercise 7: Making predictions Exercise 8: Sample from predictive density Exercise 9: Estimating test error Exercise 10: How much is an avocado?Exercise 11: Fitting the model Exercise 12: Inspecting the model Exercise 13: Optimizing the price Exercise 14: Final remarks