V
Free Download Bayesian Statistics - Bayesian Model for Healthcare Testing
Published 10/2023
Created by EDUCBA Bridging the Gap
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 11 Lectures ( 1h 26m ) | Size: 693 MB
Learn to solve statistical problems using Bayesian Statistics and MS Excel
What you'll learn
Get hands-on exposure to Bayesian Statistics
Learn to solve case studies in Excel for any statistical model
Entire concepts of Bayesian Statistics
Hands-on experience of solving statistical problems
Requirements
Prior knowledge of MS Excel is required
Basic knowledge of statistics
Description
Bayesian statistics is a particular approach to applying probability to statistical problems. It provides us with mathematical tools to update our beliefs about random events in light of seeing new data or evidence about those events. Bayesian statistics is an approach to data analysis based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. In particular Bayesian inference interprets probability as a measure of believability or confidence that an individual may possess about the occurance of a particular event.Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.Through this training we are going to apply Bayesian methods to A/B testing and also use adaptive algorithms to improve A/B testing performance.
Who this course is for
Anyone who wants to learn about data and analytics
Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers
Homepage
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