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  3. Bayesian Data Analysis in Python
  • 1

    The Bayesian way

    Gratuito

    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.

  • 2

    Bayesian estimation

    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.

  • 3

    Bayesian inference

    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.

  • 4

    Bayesian linear regression with pyMC3

    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!

Initializing

Exercicio

Bayesian spam filter

Well done on the previous exercise! Let's now tackle the famous Bayes' Theorem and use it for a simple but important task: spam detection.

While browsing your inbox, you have figured out that quite a few of the emails you would rather not waste your time on reading contain exclamatory statements, such as "BUY NOW!!!". You start thinking that the presence of three exclamation marks next to each other might be a good spam predictor! Hence you've prepared a DataFrame called emails with two variables: spam, whether the email was spam, and contains_3_exlc, whether it contains the string "!!!". The head of the data looks like this:

     spam    contains_3_excl
0    False             False
1    False             False
2    True              False
3    False             False
4    False             False

Your job is to calculate the probability of the email being spam given that it contains three exclamation marks. Let's tackle it step by step! Here is Bayes' formula for your reference:

$$P(A|B) = \frac{P(B|A) * P(A)}{P(B)}$$

Instruções 1/4

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  • Calculate the unconditional probability of the email being spam, assign it to the variable p_spam and print it.