Course Ideally, this text would be used in a one-year course in probability models. Other possible courses would be a one-semester course in introductory probability theory (involving Chapters 1–3 …

In this text, we will develop a more precise understanding of what it means to say there is a 40% chance of rain tomorrow. We will learn how to work with ideas of randomness, probability, expected value, …

Understand the concept of a continuous probability model and a continuous random variable. Understand the Inverse Transformation Method for simulating a random variable. Understand the …

But probabilistic modeling is so important that we're going to spend almost the last third of the course on it. This lecture introduces some of the key principles.

Probability modelling: select family of possible probability measures. Make best match between mathematics, real world. Coin tossing problem: many possible probability measures on Ω. For n = 3, …

probability model is used to mathematically model situations that involve some level of uncertainty. For example, analysis of golf and other sports, population studies, and analysis of wild re management …

Predictive modeling of a categorical outcome is simplified when there are only 2 classes. Many multi-class problems can be addressed by solving a set of binary classification problems (e.g., one-vs-rest).