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.

Chi è Bayes? Che cos'è Bayes?

Bayesiani vs. Frequentisti

Distribuzioni di probabilità

Probabilità e teorema di Bayes

Giochiamo a carte

Filtro antispam bayesiano

Cosa dice il test?

Assaporando Bayes

Lanciare una moneta

Più lanci, più impari

Ehi, questa moneta è equa?

The Bayesian way

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.

Under the Bayesian hood

Towards grid approximation

Grid approximation without prior knowledge

Updating posterior belief

Prior belief

The truth of the prior

Picking the right prior

Simulating posterior draws

Reporting Bayesian results

Point estimates

Highest Posterior Density credible intervals

The meaning of credibility

Bayesian estimation

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.

A/B testing

Simulate beta posterior

Posterior click rates

A or B, and how sure are we?

How bad can it be?

Decision analysis

Decision analysis: cost

Decision analysis: profit

Regression and forecasting

Defining a Bayesian regression model

Analyzing regression parameters

Predictive distribution

Bayesian inference

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!

Markov Chain Monte Carlo and model fitting

Markov Chain Monte Carlo

Sampling posterior draws

Interpreting results and comparing models

Inspecting posterior draws

Comparing models with WAIC

Making predictions

Sample from predictive density

Estimating test error

How much is an avocado?

Fitting the model

Inspecting the model

Optimizing the price

Final remarks

Bayesian linear regression with pyMC3

Ads Data

Bikes Data

L’analisi dei dati bayesiana è un metodo di inferenza statistica sempre più diffuso, che permette di determinare probabilità condizionate senza dover fare affidamento su costanti fisse come livelli di confidenza o p-value. In questo corso imparerai come funziona l’analisi bayesiana, in cosa differisce dall’approccio classico e perché è una parte indispensabile del tuo toolkit di data science. Metterai le mani su A/B testing, analisi decisionale e modelli di regressione lineare con un approccio bayesiano, analizzando dati reali di pubblicità, vendite e noleggio di biciclette. Infine, lavorerai con la libreria PyMC3, che ti aiuterà a progettare, adattare e interpretare modelli bayesiani.

Introduction to Statistics in Python

Scopri i vantaggi dell'analisi bayesiana e applicala a casi d'uso reali!

Analisi dei dati bayesiana in Python

Scopri tutti i vantaggi dell'analisi bayesiana dei dati e usala in un sacco di casi reali!

Chi è Bayes? Che cos'è Bayes?

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