Beslissingsanalyse: winst

Goed gedaan met het vertalen van de posterior-klikratio’s naar kostendistributies! Ondertussen is er nieuw bedrijfsbeleid ingevoerd. Vanaf nu is het doel van de marketingafdeling niet meer om de kosten van campagnes te minimaliseren — dat werkte niet goed — maar om de winst te maximaliseren. Kun je je bevindingen daarop aanpassen, wetende dat de verwachte opbrengst per klik van een mobiele advertentie $3.4 is en die van een desktopadvertentie $3? Om de winst te berekenen, tel je eerst de opbrengst van alle kliks op en trek je daar vervolgens de bijbehorende kosten van af.

Alles wat je in de vorige oefening hebt berekend staat in je werkruimte: het ads_cost-woordenboek en de aantallen-klikdistributies: clothes_num_clicks en sneakers_num_clicks.

Deze oefening maakt deel uit van de cursus

Bayesian Data Analysis in Python

Cursus bekijken

Oefeninstructies

Maak een woordenboek ads_profit met vier keys: clothes_mobile, sneakers_mobile, clothes_desktop en sneakers_sneakers, die elk de winstverdeling van de bijbehorende kliks bevatten.
Maak een forest plot van ads_proft met een geloofwaardigheidsinterval van 99%.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Calculate profit distributions for each product and platform
ads_profit = {
    "clothes_mobile": ____,
    "sneakers_mobile": ____,
    "clothes_desktop": ____,
    "sneakers_desktop": ____,
}

# Draw a forest plot of ads_profit
____
plt.show()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Bayesian Data Analysis in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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-testen Exercise 2: Simuleer beta-posterieur Exercise 3: Posterior klikpercentages Exercise 4: A of B, en hoe zeker zijn we?Exercise 5: Hoe erg kan het zijn?Exercise 6: Beslissingsanalyse Exercise 7: Beslissingsanalyse: kosten Exercise 8: Beslissingsanalyse: winst

Huidige oefening

Exercise 9: Regressie en forecasting Exercise 10: Een Bayesiaans regressiemodel definiëren Exercise 11: Regressieparameters analyseren Exercise 12: Predictieve verdeling

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