Memasang model

Anda dapat menggunakan model regresi linear untuk mengestimasi elastisitas harga alpukat. Rumus regresinya adalah:

Di sini, \(\beta_1\) merupakan elastisitas harga, yakni dampak harga terhadap penjualan. Anda akan mengasumsikan bahwa elastisitasnya sama untuk alpukat reguler dan organik. Anda juga memperkirakan nilainya negatif: semakin tinggi harga, semakin rendah penjualan—ini berlaku untuk sebagian besar barang. Untuk memasukkan pengetahuan awal ini ke dalam model, Anda memutuskan menggunakan distribusi normal dengan mean -80 sebagai prior untuk harga. Bagaimana Anda akan membangun model seperti ini?

CATATAN: Ingat bahwa pemanggilan pm.sample() untuk pertama kali dalam sesi Python baru memerlukan waktu, karena kode Python dikompilasi ke C di balik layar. Untuk menghemat waktu, kami hanya meminta Anda menulis kode yang benar tanpa perlu mengeksekusinya.

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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 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 dan pemodelan Exercise 2: Markov Chain Monte Carlo Exercise 3: Melakukan sampling dari posterior Exercise 4: Menafsirkan hasil dan membandingkan model Exercise 5: Memeriksa sampel posterior Exercise 6: Membandingkan model dengan WAIC Exercise 7: Membuat prediksi Exercise 8: Sampel dari kerapatan prediktif Exercise 9: Mengestimasi galat uji Exercise 10: Berapa harga sebuah alpukat?Exercise 11: Memasang model

Latihan Saat Ini

Exercise 12: Memeriksa model Exercise 13: Mengoptimalkan harga Exercise 14: Catatan penutup