This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your a
This module covers two sample confidence intervals in more depth, and confidence intervals for population variances and proportions. We will also learn how to develop confidence intervals for parameters of interest in non-normal distributions. Statistical Inference Data Science Estimation Maximum Likelihood...
727 vacancies. Candidates were shortlisted for Tier 2 based on Tier 1 scores, and category-wise cutoff marks were released. Now, SSC has issued an important notice as two legal cases have been filed challenging the result. Along with this...
📝 All of Statistics: A Concise Course in Statistical Inference - Larry Wasserman Statistical Learning 📝 An Introduction to Statistical Learning with Applications in R - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani 📝 The Elements of Statistical Learning - Trevor Hastie, Robert...
hegel ⚠️— A static type checker for JavaScript with a bias on type inference and strong type systems. jshint ℹ️— Detect errors and potential problems in JavaScript code and enforce your team's coding conventions. JSLint ℹ️— The JavaScript Code Quality Tool. JSPrime ⚠️...
(2024 ACL Findings) Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data. Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng. [pdf] (2024 arXiv) LLMs Are Prone to Fallacies in Causal Inference. Nitish Jos...
Answer to: List the steps in the classical decision-making model and the two assumptions of the model. Are these assumptions realistic in an...
Thanks to the collaboration of the two authors, a profound knowledge of already existing references and management models can be drawn upon. Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also...
For example, following Pearl’s DO-Calculus, a symbolic computation step is first executed for causal identification, turning a causal question into a statistical estimation problem, which can then be solved by fitting deep neural nets. In this work, the authors combine causal inference and neural...
Louis. He wrote extensively on statistical mechanics and on foundations of probability and statistical inference, initiating in 1957 the maximum entropy interpretation of thermodynamics as being a particular application of more general Bayesian/information theory techniques (although he argued this was ...