Advanced Probability: データの不確実性

This chapter introduces you to probability concepts that help uncover interactions between variables. By exploring multivariate distributions, conditional probability, and Markov Chains, you will gain insights into how probability-driven models can predict customer behavior, optimize strategies, and assess risks. These tools provide a solid foundation for making data-driven business decisions in uncertainty.

Chapter 2 focuses on interpreting and managing uncertainty with respect to business outcomes. Learners will learn about common techniques like expected value calculations, confidence and prediction intervals, scenario analysis and sensitivity analysis.

In the final chapter, you will explore how simulation techniques can enhance decision-making in the presence of uncertainty. You will learn to apply resampling methods, Monte Carlo simulations, and decision trees to estimate uncertainty, assess risks, and visualize strategic choices. By integrating these techniques, you will develop the ability to synthesize insights and make data-driven recommendations in business scenarios.