Uncertainty Analysis in LCA

Related lectures (29)

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Covers the propagation of uncertainty through the generation of probability density functions.

Explores Taylor methods, point estimates, Monte Carlo methods, and uncertainties in civil engineering.

Discusses estimation and propagation of uncertainty in random variables and the importance of managing uncertainty in statistical analysis.

Covers statistical analysis, emphasizing data exploration and inference to quantify uncertainty and draw conclusions.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.

Explains the geometric and negative binomial distributions and their properties.

Covers the properties of the exponential family and the estimation of parameters.

Covers the basics of probability theory, random variables, joint probability, and inference.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.