Before Memmo my notes were scattered across PDFs. Now a workspace pulls everything into one place — I see exactly what's still left to study.
This book presents a rigorous exposition of probability theory for a variety of applications. The first part of the book is a self-contained account of the fundamentals. Material suitable for advanced study is then developed from the basic concepts. Emphasis is placed on examples, sound interpretation of results and scope for applications.
A distinctive feature of the book is that it discusses modern applications seldom covered in traditional texts. Two cases in point are risk theory (or comparison of distributions) and stochastic optimization. The book also includes some recent developments of probability theory, for example limit theorems for sums of dependent variables, nonlinear and nonclassical limit theorems. Simplified proofs and a unified approach to the exposition of many results are other key features.
The book may be used as a textbook for graduate students and advanced undergraduates, and as a work of reference.
Before Memmo my notes were scattered across PDFs. Now a workspace pulls everything into one place — I see exactly what's still left to study.
Memmo's summaries are gold before exams. I don't have to re-read 800 pages two weeks before — just the important parts.
The AI chat has saved me the night before an exam more than once. I just keep asking until I get it — no waiting on a study group to reply.
The quizzes hit exactly what I need to know. Memmo tracks what I get stuck on — so I only practice what's worth it.
Flashcards with spaced repetition are magic. Memmo knows when I'm about to forget something and brings it back.
The AI podcasts are my favorite. I listen on my way to school and get a recap without sitting at a computer.
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