Tag: Bayesian

Yale University – PhD in Statistics and Data Science

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From Yale: “The Department offers a broad training program comprised of the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods.”

Bayesian Modelling in Python

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“Welcome to ‘Bayesian Modelling in Python’ – a tutorial for those interested in learning how to apply bayesian modelling techniques in python (PYMC3). This tutorial doesn’t aim to be a bayesian statistics tutorial – but rather a programming cookbook for those who understand the fundamental of bayesian statistics and want to learn how to build bayesian models using python. The tutorial sections and topics can be seen below. Contents Introduction Motivation … Continue Reading

Bayesian machine learning

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From FastML: “So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together – we know it took us a while. This article is an introduction we wish we had back then.” This article covers the following topics: Bayesians and Frequentists Priors, updates, and posteriors Inferring model parameters from data Model vs inference Statistical modelling … Continue Reading

Bayesian Data Analysis

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From Amazon: “Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors–all leaders in the statistics community–introduce … Continue Reading

Data Science and Machine Learning with Python – Hands On!

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From Udemy: “Become a data scientists in the tech industry! Comprehensive data mining and machine learning course with Python & Spark. What Will I Learn? Develop using iPython notebooks Understand statistical measures such as standard deviation Visualize data distributions, probability mass functions, and probability density functions Visualize data with matplotlib Use covariance and correlation metrics Apply conditional probability for finding correlated features Use Bayes’ Theorem to identify false positives Make predictions … Continue Reading

Bayesian Statistics

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This is course 4 of 5 in the Statistics with R Specialization. From Coursera: “This course describes Bayesian statistics, in which one’s inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show … Continue Reading

Introduction to Probability and Data

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This is course 1 of 5 in the Statistics with R Specialization. From Coursera: “This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes’ rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will … Continue Reading

Predictive Analytics World – San Francisco

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2017 Event: May 14-18, 2017 From Predictive Analytics World: Predictive Analytics World is the leading cross-vendor event for predictive analytics professionals, managers and commercial practitioners. he only conference of its kind, Predictive Analytics World delivers vendor-neutral sessions across verticals such as banking, financial services, e-commerce, entertainment, government, healthcare, manufacturing, high technology, insurance, non-profits, publishing, and retail. Overview of PAW Conference 2017 Speakers  

Machine Learning Cheat Sheet

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This cheat sheet contains many classical equations and diagrams on machine learning, which will help you quickly recall knowledge and ideas in machine learning.

Think Bayes

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Use your existing programming skills to learn and understand Bayesian statistics. Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing.