Analisis keputusan: biaya

Perjalanan Anda di bidang pemasaran berlanjut. Anda telah menghitung laju klik posterior untuk iklan pakaian dan sepatu kets, yang tersedia di workspace Anda sebagai clothes_posterior dan sneakers_posteriors. Namun, atasan Anda tidak tertarik pada distribusi laju klik. Mereka ingin mengetahui berapa biaya untuk meluncurkan kampanye iklan kepada 10'000 pengguna. Mitra periklanan perusahaan mengenakan biaya $2.5 per klik di perangkat seluler dan $2 di perangkat desktop. Atasan Anda tertarik pada biaya kampanye untuk setiap produk (pakaian dan sepatu kets) di setiap platform (seluler dan desktop): total empat besaran.

Mari bandingkan keempat biaya posterior ini menggunakan forest plot dari pymc3, yang telah diimpor untuk Anda sebagai pm.

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate distributions of the numbers of clicks for clothes and sneakers
clothes_num_clicks = ____
sneakers_num_clicks = ____

Edit dan Jalankan Kode

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: Pengujian A/B Exercise 2: Simulasikan posterior beta Exercise 3: Laju klik posterior Exercise 4: A atau B, dan seberapa yakinkah kita?Exercise 5: Seberapa buruk kemungkinan terburuknya?Exercise 6: Analisis keputusan Exercise 7: Analisis keputusan: biaya

Latihan Saat Ini

Exercise 8: Analisis keputusan: laba Exercise 9: Regresi dan peramalan Exercise 10: Mendefinisikan model regresi Bayesian Exercise 11: Menganalisis parameter regresi Exercise 12: Distribusi prediktif

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