Memperbarui keyakinan posterior

Kerja bagus dalam mengestimasi distribusi posterior untuk laju efektivitas pada latihan sebelumnya! Sayangnya, karena sampel data yang kecil, distribusi tersebut cukup lebar, yang menandakan ketidakpastian besar terkait kualitas obat. Untungnya, pengujian obat berlanjut, dan sekelompok 12 pasien sakit lainnya telah diobati, 10 di antaranya sembuh. Kita perlu memperbarui distribusi posterior dengan data baru ini!

Ini mudah dilakukan dengan pendekatan Bayesian. Kita cukup menjalankan pendekatan grid seperti sebelumnya, tetapi dengan prior yang berbeda. Kita dapat menggunakan semua pengetahuan tentang laju efektivitas (yang diwakili oleh distribusi posterior dari latihan sebelumnya) sebagai prior baru! Lalu, kita hitung ulang likelihood untuk data baru, dan peroleh posterior yang baru!

DataFrame yang Anda buat pada latihan sebelumnya, df, tersedia di workspace dan binom telah diimpor untuk Anda dari scipy.stats.

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Assign old posterior to new prior and calculate likelihood
df["new_prior"] = ____
df["new_likelihood"] = ____(df["num_patients_cured"], ____, df["efficacy_rate"])

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: Membongkar dapur Bayesian Exercise 2: Menuju pendekatan grid Exercise 3: Pendekatan grid tanpa pengetahuan awal Exercise 4: Memperbarui keyakinan posterior

Latihan Saat Ini

Exercise 5: Kepercayaan awal Exercise 6: Kebenaran tentang prior Exercise 7: Memilih prior yang tepat Exercise 8: Mensimulasikan pengambilan sampel posterior Exercise 9: Melaporkan hasil Bayesian Exercise 10: Estimasi titik Exercise 11: Interval kredibel Highest Posterior Density Exercise 12: Makna kredibilitas

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 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