frequentist-inference

General-purpose code to compute Feldman-Cousins confidence intervals

Content from the course "Principles and Applications in Statistical Analysis (52221)" at The Hebrew University of Jerusalem, in the Department of Statistics and Data Science.

This project analyses and find the better noise model for the spatial tunneling current obtained from surface tunneling microscope data using both Frequentist and Bayesian statistical inference. We mainly analyse Gaussian and Poisson noise. This project is part of Statistics and Data Analysis for Physical Science (PHY5132) course (Vasanth - 2026).

The Bayesian KMO index is a novel re-conceptualization of the Kaiser-Meyer-Olkin (KMO) index that enables researchers to incorporate prior information and make coherent probabilistic statements about the sampling adequacy of a data matrix.

The purpose of this case study is to apply the concepts associated with Frequentist inference in Python. Frequentist inference is the process of deriving conclusions about an underlying distribution via the observation of data.

This project develops a Bayesian data analysis to investigate and quantify how waste per capita is influenced by income and the adoption of Pay-As-You-Throw fee schemes, while accounting for inherently different socioeconomic contexts across Italy.