Concept

Algorithms for calculating variance

Related lectures (23)

Covers the theory of Brownian motion, diffusion, and random walks, with a focus on Einstein's theory for one-dimensional motion.

Explores the Mean-Variance case, Market Equilibrium, CAPM, and Efficient Market Hypothesis.

Delves into the complexities of extending from finite variance to finite expectation assumptions.

Explains Gaussian functions, modeling data, likelihood function, and maximum likelihood optimization.

Explores mean-variance efficient portfolios, factor models, and market efficiency in investment management.

Covers Gaussian Mixture Regression principles, modeling joint and conditional densities for multimodal datasets.

Explores factor models in finance, covering mean-variance portfolios, size and value anomalies, and momentum strategies.

Explores financial economics, mean-variance portfolio choice, asset valuation, efficient frontiers, and risk-aversion impact.

Explores risk management concepts, including VaR, ES, and measurement methods.

Covers risk measures, VaR, ES computation, backtesting, and multivariate distributions for portfolio risk assessment.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Explores Gaussian distributions, moments, and variance calculations in probability theory.

Explores autoregressive models for time series analysis, covering AR(1), AR(2), identification, and MA models.

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Explores ergodicity in continuous-time stochastic processes and its properties as time approaches infinity.

Explores constructing a probability model, random sampling, variance calculation, and allocation optimization in experiments.

Explores the concept of variance in physico-chemical systems and its applications in chemical equilibria.

Covers advanced topics in probability theory, focusing on geometric distribution and variance calculations.

Explores Value-at-Risk and Expected Shortfall as key risk measures in financial risk management.

Explores stratified sampling, dividing a population into subgroups for more accurate sampling.