Bayesian Statistics: Techniques and Models

Start Date: 05/31/2020

Course Type: Common Course

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About Course

This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution. We will use the open-source, freely available software R (some experience is assumed, e.g., completing the previous course in R) and JAGS (no experience required). We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer demonstrations provide concrete, practical walkthroughs. Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data.

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Course Introduction

Bayesian Statistics: Techniques and Models Bayesian statistics is the study of statistical inference techniques, using experiments and mathematical modeling to derive meaningful conclusions. Bayesian statistics aims to bridge the gap between the exploratory methods of statistics and the deep modeling and inference required for accurate decision making. Through this course you will learn how to use Bayesian statistics in Python 3. This will allow you to write statistical inference and model based programs, which are two of the most popular programming languages in the Python universe. This is the fourth course in the specialization. You’ll need to complete the final course (Probability and Statistics) to take full advantage of these features. Note: This course uses Python 3.Module 1: Bayesian Statistics Bayesian Statistics Explained Model Analysis Statistic Analysis Bibendum, Siegel, and Manners: Two Improving Lives In this course, you will learn how to recognize and augment your own strengths, learn how to use the tools of social psychology to design interventions that will have the greatest impact, and meet your own personal goals. Through self-assessments, you’ll also examine your own behaviors and develop a plan for change. This course is offered through the University of Copenhagen's SAGE program, which brings together scholars with international experience in mental health, addiction, and human rights. The course focus is particularly important for learners who aspire to

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Gibbs Sampling Bayesian Statistics Bayesian Inference R Programming

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