A simple explanation of the Pearson correlation coefficient with examples
A step-by-step guide to catching real anomalies without drowning in false alerts.
This article details a hands-on approach to modeling rare events in time series data using Python. It covers data exploration, defining extreme events, fitting distributions (GEV, Weibull, Gumbel), and evaluating model performance using metrics like log-likelihood, AIC, and BIC. The example uses weather data and provides code snippets for implementation.
Explores the role of conditional probability in understanding events and Bayes' theorem, with examples in regression analysis and everyday scenarios, demonstrating how our biological tissue runs probabilistic machinery.
This article explains the PCA algorithm and its implementation in Python. It covers key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues. The tutorial aims to provide a solid understanding of the algorithm's inner workings and its application for dealing with high-dimensional data and the curse of dimensionality.
‘I’ve been to Bali too’ (and I will be going back): are terrorist shocks to Bali’s tourist arrivals permanent or transitory?,”
sub-populations that have different variabilities from others. Here "variability" could be quantified by the variance or any other measure of statistical dispersion
