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Bayesian Statistics Fundamentals

Overview Bayesian Statistics Fundamentals is designed to introduce students to the core concepts of Bayesian statistics, contrasting it with the frequentist …

Bayesian Statistics Fundamentals

Bayesian Statistics Fundamentals

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Bayesian Statistics Fundamentals Overview

Bayesian Statistics Fundamentals is designed to introduce students to the core concepts of Bayesian statistics, contrasting it with the frequentist approach. Through a blend of theoretical understanding and practical application, this course will guide learners through the principles of probability, conditional probability, and Bayes’ Theorem. By the end, students will be able to solve complex statistical problems using Bayesian methods, understand counter-intuitive results, and simulate solutions for real-world scenarios.

Learning Outcomes

  • Understand the basic principles of probability.
  • Differentiate between Bayesian and frequentist models of probability.
  • Apply conditional probability to various contexts using Venn diagrams.
  • Use tree diagrams to represent and solve conditional probability problems.
  • Solve example questions using tree diagrams.
  • Explore the relationship between conditional probability and normal distributions.
  • Identify and interpret counter-intuitive results in normal distributions.
  • Develop and apply Bayes’ Theorem in practical scenarios.
  • Solve Thomas Bayes’ puzzle using Bayesian methods.
  • Simulate solutions to statistical problems using Bayesian techniques.

Who Is This Course For

This course is designed for individuals seeking to deepen their knowledge and understanding of the field. Whether you are a beginner looking to build a strong foundation or an experienced professional aiming to expand their proficiency, this course caters to a diverse audience. The course is particularly beneficial for those who want to stay current with industry trends, enhance their career prospects, or simply explore a new interest. Through a combination of theoretical insights and practical applications, participants will gain a comprehensive understanding that can be directly applied to real-world scenarios.

Eligibility Requirements

To Enrol In This Course, You Should Have A Basic Understanding Of Statistics And Mathematics. While Prior Experience With Probability Concepts Is Beneficial, It Is Not Mandatory. The Course Builds From Foundational Concepts To More Advanced Applications, Making It Accessible To Learners At Various Levels. By Completing This Course, You Will Gain Valuable Skills And Knowledge, Enhancing Your Analytical Abilities And Opening Up New Professional Opportunities. Join Us And Start Your Journey Into The Fascinating World Of Bayesian Statistics!

Entry Requirements

  • Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
  • Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
  • Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
  • Numeracy Skills: Basic numeracy skills are required for understanding nutritional data and dietary planning.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path

A strong foundation in Bayesian statistics opens up numerous career opportunities. As data becomes increasingly central to decision-making across various industries, professionals skilled in Bayesian methods are in high demand. This course will equip you with the analytical skills needed to pursue careers in data science, research, and analytics, positioning you for roles that require advanced statistical knowledge and the ability to solve complex problems.

 

Course Curriculum

Introduction
Introduction 00:01:00
What Is Probability?
Bayesian vs Frequentist models of probability 00:09:00
Conditional Probability
Conditional Probability Intro 00:03:00
Conditional Probability on Venn Diagrams 00:07:00
Conditional Probability on Tree Diagrams 00:04:00
Tree Diagram Example Question 00:05:00
Conditional Probability and Normal Distributions 00:04:00
Counter Intuitive Results with the Normal Distribution 00:08:00
Bayes' Theorem
Developing Bayes’ Theorem part 1 00:07:00
Developing Bayes’ Theorem part 2 00:08:00
Thomas Bayes’ Puzzle 00:06:00
A Bayesian Solution to the Puzzle 00:07:00
Simulating a Solution 00:09:00

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