Question | Answer |
\sigma-algebra | |
probability measure | |
measurable function | |
random variable/random vector | |
probability law/probability distribution | |
cumulative distribution function | |
probability density function | |
random variables X and Y are identically distributed if... | |
sub-\sigma-algebra | |
independent (\sigma-algebra) | |
independence (random variables) | |
Lebesgue integration Step 1: Indicator functions | |
Lebesgue Integration Step 2: simple functions | |
Lebesgue Integration Step 3: non-negative random variables | |
Lebesgue Integration Step 4: arbitrary random variables | |
integrable | |
null set | |
Proof - see lecture notes | |
Properties of the integral (five) | linearity domination triangle inequality set bounded integrability change of variable |
Properties of the Integral/Expectation 1. Linearity | |
Properties of the Integral/Expectation 2. Domination | |
Properties of the Integral/Expectation 3. the triangle inequality | |
Properties of the Integral/Expectation 4. Set bounded integrability | |
Properties of the Integral/Expectation 5. Change of variable | |
Dominated Convergence Theorem | |
Monotone Convergence Theorem | |
nth moment mean variance | |
moment generating function | |
Almost sure convergence | |
mean square convergence | |
Chebychev's Inequality | |
Cauchy-Schwarz inequality | |
convex function | |
Jensen's inequality | |
Proof - see notes | |
Absolutely Continuous measure Equivalent measures | |
Radon-Nikodym | |
Radon-Nikodym derivative | |
stochastic process | |
integrable stochastic process |
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