Martingale theory | Probability theorems

Martingale representation theorem

In probability theory, the martingale representation theorem states that a random variable that is measurable with respect to the filtration generated by a Brownian motion can be written in terms of an Itô integral with respect to this Brownian motion. The theorem only asserts the existence of the representation and does not help to find it explicitly; it is possible in many cases to determine the form of the representation using Malliavin calculus. Similar theorems also exist for martingales on filtrations induced by jump processes, for example, by Markov chains. (Wikipedia).

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RT6. Representations on Function Spaces

Representation Theory: We note how to transfer a group action of a group G on a set X to a group action on F(X), the functions on X. Because F(X) is a vector space, we obtain a representation of the group, and we can apply previous techniques. In particular, the group acts on itself in

From playlist Representation Theory

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Function Space and series

In this video, I explain function space and how to change the basis vectors we use to describe function. This lead us to a different understanding of Taylor series, Fourier series and most series. I also explain the Heisenberg uncertainty principle using function space. Additionnal video

From playlist Summer of Math Exposition Youtube Videos

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This lecture is an introduction to representation theory of finite groups. We define linear and permutation representations, and give some examples for the icosahedral group. We then discuss the problem of writing a representation as a sum of smaller ones, which leads to the concept of irr

From playlist Representation theory

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From playlist Representation theory

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Mike Boyle - Nonnegative matrices : Perron Frobenius theory and related algebra (Part 1)

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From playlist École d’été 2013 - Théorie des nombres et dynamique

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C73 Introducing the theorem of Frobenius

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From playlist Differential Equations

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Cayley-Hamilton Theorem: General Case

Matrix Theory: We state and prove the Cayley-Hamilton Theorem over a general field F. That is, we show each square matrix with entries in F satisfies its characteristic polynomial. We consider the special cases of diagonal and companion matrices before giving the proof.

From playlist Matrix Theory

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From playlist École d’été 2013 - Théorie des nombres et dynamique

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Cayley-Hamilton Theorem Example 2

Matrix Theory: Let A be the 3x3 matrix A = [1 2 2 / 2 0 1 / 1 3 4] with entries in the field Z/5. We verify the Cayley-Hamilton Theorem for A and compute the inverse of I + A using a geometric power series.

From playlist Matrix Theory

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From playlist Probability and Statistics

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From playlist Financial Mathematics

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From playlist Quantitative Finance Seminar @ SNS

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From playlist Financial Mathematics

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Fin Math L5-2: A simple exchange rate model

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From playlist Financial Mathematics

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Experimentation with Temporal Interference: by Peter W Glynn

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From playlist Advances in Applied Probability 2019

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Hans Föllmer: Entropy, energy, and optimal couplings on Wiener space

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From playlist Probability and Statistics

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Peter Imkeller: An introduction to BSDE

Abstract: Backward stochastic differential equations have been a very successful and active tool for stochastic finance and insurance for some decades. More generally they serve as a central method in applications of control theory in many areas. We introduce BSDE by looking at a simple ut

From playlist Probability and Statistics

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Martin Larsson: Affine Volterra processes and models for rough volatility

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From playlist Probability and Statistics

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Xiaolu Tan: On the martingale optimal transport duality in the Skorokhod space

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From playlist HIM Lectures 2015

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Boris Adamczewski: Mahler's method in several variables

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From playlist Combinatorics

Related pages

Adapted process | Filtration (probability theory) | Wiener process | Augmented filtration | Probability theory | Martingale (probability theory) | Volatility (finance) | Predictable process | Jump process | Markov chain | Filtered probability space | Malliavin calculus