Menuju pendekatan grid

Selamat! Anda baru saja direkrut sebagai analis data di Departemen Kesehatan pemerintah Anda. Kabinet sedang mempertimbangkan pembelian obat baru untuk melawan virus yang mematikan dan menular. Namun, ada keraguan mengenai seberapa efektif obat baru tersebut terhadap virus. Tugas Anda adalah mengestimasi tingkat efikasi obat, yaitu persentase pasien yang disembuhkan oleh obat tersebut.

Sebuah eksperimen cepat dilakukan, di mana 10 pasien sakit diberi pengobatan dengan obat tersebut. Setelah Anda mengetahui berapa banyak yang sembuh, Anda dapat menggunakan distribusi binomial dengan pasien yang sembuh sebagai "keberhasilan" dan tingkat efikasi sebagai "probabilitas keberhasilan". Sambil menunggu hasil eksperimen, Anda memutuskan untuk menyiapkan grid parameter.

numpy dan pandas telah diimpor untuk Anda masing-masing sebagai np dan pd.

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

Petunjuk latihan

Dengan np.arange(), buat array berisi semua kemungkinan jumlah pasien yang sembuh (dari 0 hingga 10) dan simpan ke num_patients_cured.
Dengan np.arange(), buat array berisi semua kemungkinan nilai tingkat efikasi (dari 0 hingga 1, bertambah 0,01) dan simpan ke efficacy_rate.
Gabungkan num_patients_cured dan efficacy_rate ke dalam sebuah DataFrame bernama df, yang memuat semua kemungkinan kombinasi keduanya.
Tetapkan ["num_patients_cured", "efficacy_rate"] sebagai kolom df dan cetak.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create cured patients array from 1 to 10
num_patients_cured = ____

# Create efficacy rate array from 0 to 1 by 0.01
efficacy_rate = ____

# Combine the two arrays in one DataFrame
df = ____([(x, y) for x in ____ for y in ____])

# Name the columns
df.columns = ____

# Print df
print(df)

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

Latihan Saat Ini

Exercise 3: Pendekatan grid tanpa pengetahuan awal Exercise 4: Memperbarui keyakinan posterior 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