Transcription of Notes 3 : Modes of convergence
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Notes 3 : Modes of convergenceMath 733-734: Theory of ProbabilityLecturer: Sebastien RochReferences: [Wil91, Chapters ], [Dur10, Sections , ].1 Modes of convergenceLet( ,F,P)be a probability space. We will encounter various Modes of conver-gence for sequences of RVs on( ,F,P).DEF ( Modes of convergence )Let{Xn}nbe a sequence of (not necessarilyindependent) RVs and letXbe a RV. Then we have the following definitions. convergence in probability: >0,P[|Xn X|> ] 0(asn + );which we denote byXn PX. convergence almost sure:P[Xn X] = 1. convergence inLp(p 1):E|Xn X|p better understand the relationship between these different Modes of conver-gence, we will need Markov s inequality as well as the Borel-Cantelli first state these, then come back to applications of independent interest Markov s inequalityLEM (Markov s inequality)LetZ 0be a RV on( ,F,P).
Lecture 3: Modes of convergence 6 Before we give the proofs of these theorems, we discuss further applications of Markov’s inequality and the Borel-Cantelli lemmas.
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REAL ANALYSIS LECTURE NOTES, Convergence, Lecture notes, NONLINEAR PROGRAMMING LECTURE 4, LECTURE 4 CONVERGENCE, Lecture, Convergence and Divergence, Convergence and Divergence Lecture Notes, Economic Growth, Convergence of a Sequence, Monotone sequences, Lecture 18 : Improper integrals, Lecture 4 | September 11 4.1 Gradient Descent, Lecture 4 | September 11