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.

Who is Bayes? What is Bayes?

Bayesians vs. Frequentists

Probability distributions

Probability and Bayes' Theorem

Let's play cards

Bayesian spam filter

What does the test say?

Tasting the Bayes

Tossing a coin

The more you toss, the more you learn

Hey, is this coin fair?

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 dan pemodelan

Markov Chain Monte Carlo

Melakukan sampling dari posterior

Menafsirkan hasil dan membandingkan model

Memeriksa sampel posterior

Membandingkan model dengan WAIC

Membuat prediksi

Sampel dari kerapatan prediktif

Mengestimasi galat uji

Berapa harga sebuah alpukat?

Memasang model

Memeriksa model

Mengoptimalkan harga

Catatan penutup

Bayesian linear regression with pyMC3

Ads Data

Bikes Data

Analisis data Bayesian adalah metode inferensi statistika yang semakin populer, digunakan untuk menentukan probabilitas bersyarat tanpa bergantung pada konstanta tetap seperti tingkat kepercayaan atau p-value. Dalam kursus ini, Anda akan mempelajari cara kerja analisis data Bayesian, perbedaannya dari pendekatan klasik, dan mengapa metode ini menjadi bagian penting dari perlengkapan data science Anda. Anda akan mempraktikkan A/B testing, analisis keputusan, dan pemodelan regresi linear menggunakan pendekatan Bayesian saat menganalisis data periklanan, penjualan, dan penyewaan sepeda dunia nyata. Terakhir, Anda akan langsung menggunakan pustaka PyMC3, yang memudahkan Anda merancang, menyesuaikan, dan menafsirkan model Bayesian.

Introduction to Statistics in Python

Pelajari keuntungan analisis data Bayesian dan terapkan pada berbagai kasus nyata!

Analisis Data Bayesian dengan Python

Pelajari semua tentang keunggulan analisis data Bayesian, dan terapkan pada berbagai kasus nyata di dunia nyata!

Markov Chain Monte Carlo dan pemodelan

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