Bayesian Statistics: From Concept to Data Analysis

Start Date: 02/23/2020

Course Type: Common Course

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About Course

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.

Course Syllabus

In this module, we review the basics of probability and Bayes’ theorem. In Lesson 1, we introduce the different paradigms or definitions of probability and discuss why probability provides a coherent framework for dealing with uncertainty. In Lesson 2, we review the rules of conditional probability and introduce Bayes’ theorem. Lesson 3 reviews common probability distributions for discrete and continuous random variables.

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Course Introduction

Bayesian Statistics: From Concept to Data Analysis Bayesian statistics is the field that studies the foundations of statistics, especially as applied to data analysis. Bayesian statistics models the notions of probability, statistical inference, and statistical modeling, and studies the methods used to construct confidence intervals and confidence bounds for predictions. It also studies the imprecision of statistical inference, and the techniques used to explore the uncertainty of statistical modeling. Bayesian statistics is the branch of statistics that studies statistical inference, and studies the Bayesian framework for modeling and inference. It studies the statistical modeling of linear and logistic regression models, as well as the classification of models with respect to their performance in predicting outcomes. It also studies the classification of models with respect to their performance in predicting the success of interventions. It studies the statistical modeling of multivariate and cumulative logistic regression models, as well as the classification of models with respect to their performance in predicting the success of interventions for a particular intervention. It also studies the classification of models with respect to their performance in predicting the success of interventions for a particular patient or population.Bayesian Statistics Perceptrons and Signal Detection Confidence Intervals and Boundaries Conceptual Inferences and Probability Modeling Biological Decisions: From Personal Preference to Medical Intervention In this course, you will learn how to make better medical decisions using bioethics and decision-making models. You will learn how to design bioethics committees

Course Tag

Statistics Bayesian Statistics Bayesian Inference R Programming

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