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Probability Theory

Measure-theoretic probability, distributions, and stochastic processes.

13 objects

Knowledge Graph
DefinitionLemmaRemarkTheorem

Objects

DefinitionProbability Space
Probability Theory · probability.tex
The triple $(\Omega, \mathcal{F}, P)$ is a probability space if • $\Omega$ is the sample space, that is some possibly abstract set. • $\mathcal{F}$ is a $\sigma$-algebra of sets - the measurable subsets o…
DefinitionRandom Variable
Probability Theory · probability.tex
A random variable $X$ is a measurable function from the sample space $\Omega$ to $\R$ $$ X : \Omega \to \R $$ that is, the inverse of any Borel Set in $\R$ is $\mathcal{F}$-measurable: $$ X^{-1} (A) = \{\omega : X(…
DefinitionSeperating Class
Probability Theory · probability.tex
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DefinitionMetric on the Space of Probability Measures
Probability Theory · probability.tex
Given a seperating class $\mathcal{H}$ : \[ d_\mathcal{H}(F,G) = \operatorname{sup}_{h \in \mathcal{H}} | \mathbb{E}(h(F)) - \mathbb{E}(h(G)) | \]
LemmaStein's Lemma
Probability Theory · probability.tex
A real valued random variable $N$ has the standard Gaussian distribution if and only if for every test function $f : \mathbb{R} \to \mathbb{R}$ that is differentiable with $f' \in \mathcal{L}'(\gamma)$, the expe…
RemarkStein's Heuristic
Probability Theory · probability.tex
Let $F$ be a random variable such that \[ \mathbb{E}(f'(F)-Ff(f)) \approx 0 \] for a large class of test functions $f$ we want to say that this is close to the standard Gaussian. \[ L(F) \approx N(0,…
TheoremStein's Method
Probability Theory · probability.tex
Let $\gamma$ be the standard Gaussian Measure \[ \gamma(dx) = \frac{1}{} \]
DefinitionStein's Equation
Probability Theory · probability.tex
Let $N \sim N(0,1)$. Let $h : \mathbb{R} \to \mathbb{R}$ be a Borel function such that \[ \mathbb{E}(|h(N)|) < \infty \] or in other words \[ h \in \mathcal{L}(\gamma) \] The Stein Equation a…
LemmaProposition
Probability Theory · probability.tex
All solutions of Stein's Equation are of the form \[ f(x) = Ce^{x^2/2} + e^{x^2/2}\int_{-\infty}^x [h(y)-\mathbb{E}(h(N))]e^{y^2/2} dy \] In particular, denote by \[ f_h(x) = e^{x^2/2}\int_{-\i…
TheoremStein's Lemma and Metrics via Stein's Eq
Probability Theory · probability.tex
Let $\mathcal{H}$ be a seperating class and let $h \in \mathcal{H}$. Let $f_h$ be the solution to the Stein Equation associated with $h$. Then \[ f'_h(x)-xf_h(x) = h(x)-\mathbb{E}(h(N)) \] Let $…
DefinitionTV Metric
Probability Theory · probability.tex
The total variation metric is is defined as follows: \[ d_\operatorname{TV}(F,G) = \sup_{B \in \mathcal{B}(\mathbb{R})} | \mathbb{P}(F \in B) - \mathbb{P}(G \in B) \] where $\mathcal{B}(\mathbb{R})$ are the Borel …
DefinitionKolmogorov Metric
Probability Theory · probability.tex
The Kolmogorow Metric metrizes the space of probability distributions given the following definition: \[ d_{\operatorname{Kol}}(F,G) = \sup | P(F \le z) - P(G\le z) | \]
LemmaStein Bounds for TV Metric
Probability Theory · probability.tex
We take the seperating class that defined the total variation metric : \[ \mathcal{H}_{TV} = \{\mathbb{1}_B : B \in \mathcal{B}(\mathbb{R})\} \] The proposition is as follows: Let $h…