Sampel dari kerapatan prediktif

Akhirnya! Tugas Anda adalah memprediksi jumlah sepeda yang disewa per hari, dan Anda hampir sampai. Anda telah memasangkan model dan memeriksa kualitas pengambilan parameter. Anda juga telah memilih model yang lebih baik dari dua model yang bersaing berdasarkan WAIC. Sekarang, saatnya menggunakan model terbaik Anda untuk membuat prediksi!

Beberapa observasi baru yang belum pernah dilihat model telah dikumpulkan dalam DataFrame bernama bikes_test. Untuk masing-masing observasi, kita mengetahui jumlah sebenarnya sepeda yang disewa, yang akan memungkinkan kita mengevaluasi kinerja model. Pada latihan ini, Anda akan mengenal data uji dan menghasilkan pengambilan prediktif untuk setiap observasi uji. Jejak model yang telah Anda hasilkan sebelumnya tersedia sebagai trace_2, dan pymc3 telah diimpor sebagai pm. Mari membuat prediksi!

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Print bikes_test head
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: 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

Latihan Saat Ini

Exercise 9: Mengestimasi galat uji Exercise 10: Berapa harga sebuah alpukat?Exercise 11: Memasang model Exercise 12: Memeriksa model Exercise 13: Mengoptimalkan harga Exercise 14: Catatan penutup