Lecture

Uncertainty and Significant Figures

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Related lectures (64)

Logistic Regression: Modeling Binary Response Variables

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Probabilities and Statistics

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.

Hypothesis Testing: Wilks' Theorem

Explores hypothesis testing using Wilks' Theorem, likelihood ratio statistics, p-values, interval estimation, and confidence regions.

Estimation Criteria

Covers criteria for estimating parameters, emphasizing the importance of consistency, bias, variance, and efficiency of estimators.

Optimization-Based Uncertainty Quantification

Explores optimization-based uncertainty quantification for ill-posed inverse problems in the physical sciences, focusing on regularization methods and interval constructions.

Hypothesis Testing: A Different Perspective

Delves into a different perspective on hypothesis testing, emphasizing the p-value and significance levels.

Statistical Inference

Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.

Confidence Intervals and Pivotal Quantities

Explores pivotal quantities in statistics and their role in constructing confidence intervals and hypothesis tests.

Confidence Intervals: Definition and Estimation

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

Statistical Significance: Maximum Likelihood Estimation and Confidence Intervals

Explores type I and type II errors, critical values, and confidence intervals in statistical significance.

Point Estimation Methods: MOM and MLE

Explores point estimation methods like MOM and MLE, discussing bias, variance, and examples.

Stochastic Simulation: Computation and Estimation

Covers computation and estimation in stochastic simulation, focusing on generating iid replicas and optimal importance sampling.

Multi-arm Bandits: Upper Confidence Bound

Covers the Upper Confidence Bound algorithm for multi-arm bandits.

Confidence Intervals and Hypothesis Tests

Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.

Statistics: Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

VaR Model Evaluation

Discusses Monte Carlo VaR accuracy, confidence intervals, backtesting, and multivariate distributions.

Numerical Simulation of SDEs: Monte Carlo & Optimal Control

Covers Monte Carlo methods, variance reduction, and stochastic optimal control, exploring simulation techniques, efficiency, and investment dynamics.

Inference and Mixed Models

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.

Confidence Intervals and Hypothesis Testing

Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.

From signal to observables

Covers detectors' types, counting statistics, error prediction, and uncertainty estimation in measurements, emphasizing the importance of statistical tests and the optimization of experiments.