Uncertainty Analysis in LCA

Related lectures (29)

Page 2 of 3

Covers t-tests, p-values calculation, and comparison of coefficients.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

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

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Focuses on the inference of reaction kinetics in combustion, covering rules inference, sensitivity analysis, and Bayesian inference.

Explores regular exponential family models, unifying distributions like Poisson, binomial, and normal under a common framework.