Towards grid approximation

Congratulations! You have just been hired as a data analyst at your government's Department of Health. The cabinet is considering the purchase of a brand-new drug against a deadly and contagious virus. There are some doubts, however, regarding how effective the new drug is against the virus. You have been tasked with estimating the drug's efficacy rate, i.e. the percentage of patients cured by the drug.

An experiment was quickly set up in which 10 sick patients have been treated with the drug. Once you know how many of them are cured, you can use the binomial distribution with a cured patient being a "success" and the efficacy rate being the "probability of success". While you are waiting for the experiment's results, you decide to prepare the parameter grid.

numpy and pandas have been imported for you as np and pd, respectively.

This exercise is part of the course

Bayesian Data Analysis in Python

Exercise instructions

Using np.arange(), create an array of all possible numbers of patients cured (from 0 to 10) and assign it to num_patients_cured.
Using np.arange(), create an array of all possible values for the efficacy rate (from 0 to 1, by 0.01) and assign it to efficacy_rate.
Combine num_patients_cured and efficacy_rate into a DataFrame called df, listing all possible combinations of the two.
Assign ["num_patients_cured", "efficacy_rate"] to df's columns and print it.

Hands-on interactive exercise

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

# Create cured patients array from 1 to 10
num_patients_cured = ____

# Create efficacy rate array from 0 to 1 by 0.01
efficacy_rate = ____

# Combine the two arrays in one DataFrame
df = ____([(x, y) for x in ____ for y in ____])

# Name the columns
df.columns = ____

# Print df
print(df)

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

Current Exercise

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

Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. You’ll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.

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