Seberapa buruk kemungkinan terburuknya?

Anda menyimpulkan bahwa dengan probabilitas 98%, iklan pakaian memiliki rasio klik-tayang lebih tinggi daripada iklan sepatu. Ini menyarankan untuk memperluas kampanye pakaian ke audiens yang lebih besar. Namun, ada risiko 2% bahwa justru iklan sepatu yang lebih baik. Jika demikian, berapa banyak klik yang kita rugi jika kita menggulirkan kampanye pakaian?

Jawabannya adalah kerugian ekspektasian: rata-rata selisih posterior antara dua rasio klik-tayang dengan syarat iklan sepatu berkinerja lebih baik. Untuk menghitungnya, Anda hanya perlu mengambil entri pada selisih posterior ketika laju klik-tayang sepatu lebih tinggi dan hitung rata-ratanya.

Selisih posterior antara laju klik, diff, tersedia di workspace Anda. Mari kita cari tahu seberapa besar yang berisiko!

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

Petunjuk latihan

Lakukan slice pada diff untuk mengambil hanya kasus ketika nilainya negatif (menandakan laju klik sepatu lebih tinggi) dan tetapkan hasilnya ke loss.
Hitung rata-rata loss, tetapkan ke expected_loss, lalu cetak nilainya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Slice diff to take only cases where it is negative
loss = ____

# Compute and print expected loss
expected_loss = ____
print(____)

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?

Latihan Saat Ini

Exercise 6: Analisis keputusan Exercise 7: Analisis keputusan: biaya 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