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The first part introduces the fundamentals of probability theory across 7 chapters, targeting bachelor level, including event algebras, random variables, independence, conditional probabilities, moments of discrete and continuous random variables, generating functions, and limit theorems. The second part contains 10 chapters and corresponds to master level. Following a brief introduction to measure theory, this part develops more advanced topics: probability measures and their complements, distributions and moments of random variables, modes of convergence, laws of large numbers, conditional expectation, Fourier transforms and characteristic functions, Gaussian random variables, convergence of measures, convergence in distribution, discrete-time stochastic processes, martingales, and Markov chains. The reader's work is greatly facilitated by the inclusion, in every chapter, of numerous exercises, all accompanied by detailed solutions that often provide substantial extensions to the theoretical material.
From: Xavier Ouvrard [view email]
[v1]
Mon, 26 Jan 2026 15:51:24 UTC (9,604 KB)
[v2]
Wed, 28 Jan 2026 10:04:50 UTC (9,604 KB)
[v3]
Wed, 15 Apr 2026 06:34:45 UTC (10,044 KB)
[v4]
Sat, 13 Jun 2026 16:17:37 UTC (12,316 KB)
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