We may earn an affiliate commission when you visit our partners.
Pulak Ghosh
Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical Statistics including among others Parametrizations, Priors, Likelihood, Monte Carlo methods and computing Bayesian models with the exploration of Multilevel modelling.
Read more
Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical Statistics including among others Parametrizations, Priors, Likelihood, Monte Carlo methods and computing Bayesian models with the exploration of Multilevel modelling.
Read more
Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical Statistics including among others Parametrizations, Priors, Likelihood, Monte Carlo methods and computing Bayesian models with the exploration of Multilevel modelling.
This course is divided into two parts i.e. Theoretical and Empirical part of Bayesian Analytics. First three weeks cover the Theoretical part which includes how to form a prior, how to calculate a posterior and several other aspects. Rest of the weeks will cover the empirical part which explains how to compute Bayesian modelling. Completion of this course will provide you with an understanding of the Bayesian approach, the primary difference between Bayesian and Frequentist approaches and experience in data analyses.
What you'll learn
- Understand the necessary Bayesian concepts from practical point of view for better decision making.
- Learn Bayesian approach to estimate likely event outcomes, or probabilities using datasets.
- Gain “hands on” experience in creating and estimating Bayesian models using R and OPENBUGS.
Register for this course and see more details by visiting:
OpenCourser.com/course/wbc2f1/applied
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Use code at checkout.
Valid until December 4
Save on Select Programs
Stay ahead in a competitive job market and save with these programs.
Take
30%
off
EDXCYBER24
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Learning objectives
- Understand the necessary bayesian concepts from practical point of view for better decision making.
- Learn bayesian approach to estimate likely event outcomes, or probabilities using datasets.
- Gain “hands on” experience in creating and estimating bayesian models using r and openbugs.
- Understand the necessary bayesian concepts from practical point of view for better decision making.
- Learn bayesian approach to estimate likely event outcomes, or probabilities using datasets.
- Gain “hands on” experience in creating and estimating bayesian models using r and openbugs.
Syllabus
Week 01: What is Bayesian Statistics and How it is different than Classical Statistics
Foundations of Bayesian Inference
Bayes theorem
Advantages of Bayesian models
Read more
Syllabus
Week 01: What is Bayesian Statistics and How it is different than Classical Statistics
Foundations of Bayesian Inference
Bayes theorem
Advantages of Bayesian models
Read more
Why Bayesian approach is so important in Analytics
Major densities and their applications
Week 02: Bayesian analysis of Simple Models
Likelihood theory and Estimation
Parametrizations and priors
Learning from binary models
Learning from Normal Distribution
Week 03: Monte Carlo Methods
Basics of Monte carol integration
Basics of Markov chain Monte Carlo
Gibs Sampling
Week 04: Computational Bayes
Examples of Bayesian Analytics
Introduction to R and OPENBUGS for Bayesian analysis
Week 05: Bayesian Linear Models
Context for Bayesian Regression Models
Normal Linear regression
Logistic regression
Week 06: Bayesian Hierarchical Models
Introduction to Multilevel models
Exchangeability
Computation in Hierarchical Models
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners in Bayesian statistics
Provides hands-on experience with Bayesian modeling using R and OPENBUGS
Develops an understanding of the Bayesian approach to data analysis and probability estimation
Useful for learners interested in applying Bayesian statistics to real-world problems
Taught by Pulak Ghosh, who is recognized for his work in Bayesian statistics
Explored through a mix of theoretical and empirical components for a comprehensive understanding
Save this course
Save Applied Bayesian for Analytics to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Applied Bayesian for Analytics with these
activities:
Review concepts from linear algebra and calculus
Show steps
Revisit concepts from linear algebra and calculus to strengthen your mathematical foundation for Bayesian Statistics.
Browse courses on
Linear Algebra
Show steps
-
Review notes or textbooks on linear algebra and calculus.
-
Solve practice problems to refresh your understanding.
-
Consider taking a refresher course or workshop.
Review Probability Theory
Show steps
Reinforces the foundational concepts of probability theory necessary for understanding Bayesian Statistics.
Browse courses on
Probability Theory
Show steps
-
Revisit the definitions and axioms of probability.
-
Review concepts such as conditional probability, independence, and Bayes theorem.
-
Solve practice problems to strengthen understanding.
Review probability theory
Show steps
Refresh your understanding of probability theory to strengthen your foundation for Bayesian Statistics.
Browse courses on
Probability Theory
Show steps
-
Review notes or textbooks on probability theory.
-
Practice solving probability problems.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Read Bayesian Data Analysis by Andrew Gelman et al.
Show steps
Engage with one of the foundational texts on Bayesian Statistics to deepen your understanding and broaden your perspective.
View
Bayesian Data Analysis (Chapman & Hall/CRC...
on Amazon
Show steps
-
Obtain a copy of the book.
-
Set aside dedicated time for reading.
-
Read one chapter at a time, taking notes and highlighting important concepts.
-
Complete the exercises and problems in the book.
-
Discuss the book with peers or the course instructor.
Review Precalculus
Show steps
This course builds heavily on precalculus and higher. Reviewing precalculus concepts will provide a stronger foundation.
Browse courses on
Precalculus
Show steps
-
Review your class notes and materials
-
Complete practice problems
-
Take mock practice exams
Review and compile resources on Bayesian Statistics
Show steps
Gather and organize relevant resources to deepen your understanding and expand your knowledge base.
Browse courses on
Bayesian Statistics
Show steps
-
Search for online articles, books, and videos on Bayesian Statistics.
-
Review the resources and select those that are most informative and relevant.
-
Organize the resources into a collection or bibliography.
Discuss Bayesian concepts with peers
Show steps
Engage in discussions with peers to clarify concepts, share insights, and receive feedback.
Browse courses on
Bayesian Statistics
Show steps
-
Join or form a study group with other students in the course.
-
Schedule regular meetings to discuss course materials and exchange ideas.
-
Prepare for discussions by reviewing materials and preparing questions.
Practice Bayesian Inference
Show steps
Strengthens understanding of Bayesian inference techniques through repeated practice.
Browse courses on
Bayesian Inference
Show steps
-
Solve exercises involving Bayes theorem and posterior probability calculations.
-
Use online resources or textbooks for additional problems.
Learn R and OPENBUGS for Bayesian analysis
Show steps
Follow tutorials to gain practical experience in using R and OPENBUGS for Bayesian analysis.
Browse courses on
R
Show steps
-
Find tutorials on R and OPENBUGS.
-
Follow the tutorials step-by-step.
-
Complete the exercises or projects provided in the tutorials.
Discuss Bayesian Applications
Show steps
Fosters collaboration and exchange of ideas on practical applications of Bayesian Statistics.
Show steps
-
Participate in online forums or discussion groups focused on Bayesian Statistics.
-
Share experiences and insights related to Bayesian modeling and analysis.
Bayesian modeling exercises
Show steps
Solve Bayesian modeling exercises to solidify your understanding of the concepts and techniques.
Browse courses on
Bayesian Modeling
Show steps
-
Find practice problems or exercises online or in textbooks.
-
Attempt to solve the problems independently.
-
Check your solutions against provided solutions or consult with the course instructor.
Create a Bayesian Model
Show steps
Provides hands-on experience in applying Bayesian concepts by creating a custom model.
Browse courses on
Bayesian Analysis
Show steps
-
Define the problem and gather necessary data.
-
Choose appropriate priors and likelihood functions.
-
Implement the model using a programming language or software.
-
Analyze the results and evaluate the model's performance.
Create a Bayesian analysis project
Show steps
Apply your skills to develop a comprehensive Bayesian analysis project, demonstrating your understanding of the concepts.
Browse courses on
Bayesian Statistics
Show steps
-
Identify a suitable dataset and research question.
-
Develop a Bayesian model to address the research question.
-
Implement the model in R or OPENBUGS and perform the analysis.
-
Interpret the results and draw conclusions.
-
Present the project in a report or presentation.
Explore Markov Chain Monte Carlo Methods
Show steps
Deepens knowledge of Markov Chain Monte Carlo methods used in Bayesian computations.
Browse courses on
Markov Chain Monte Carlo
Show steps
-
Follow online tutorials or read research papers on MCMC.
-
Implement MCMC algorithms in a programming language.
-
Analyze the convergence and efficiency of MCMC simulations.
Review concepts from linear algebra and calculus
Show steps
Revisit concepts from linear algebra and calculus to strengthen your mathematical foundation for Bayesian Statistics.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes or textbooks on linear algebra and calculus.
- Solve practice problems to refresh your understanding.
- Consider taking a refresher course or workshop.
Review Probability Theory
Show steps
Reinforces the foundational concepts of probability theory necessary for understanding Bayesian Statistics.
Browse courses on
Probability Theory
Show steps
Show steps
Show steps
- Revisit the definitions and axioms of probability.
- Review concepts such as conditional probability, independence, and Bayes theorem.
- Solve practice problems to strengthen understanding.
Review probability theory
Show steps
Refresh your understanding of probability theory to strengthen your foundation for Bayesian Statistics.
Browse courses on
Probability Theory
Show steps
Show steps
Show steps
- Review notes or textbooks on probability theory.
- Practice solving probability problems.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Read Bayesian Data Analysis by Andrew Gelman et al.
Show steps
Engage with one of the foundational texts on Bayesian Statistics to deepen your understanding and broaden your perspective.
View
Bayesian Data Analysis (Chapman & Hall/CRC...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Set aside dedicated time for reading.
- Read one chapter at a time, taking notes and highlighting important concepts.
- Complete the exercises and problems in the book.
- Discuss the book with peers or the course instructor.
Review Precalculus
Show steps
This course builds heavily on precalculus and higher. Reviewing precalculus concepts will provide a stronger foundation.
Browse courses on
Precalculus
Show steps
Show steps
Show steps
- Review your class notes and materials
- Complete practice problems
- Take mock practice exams
Review and compile resources on Bayesian Statistics
Show steps
Gather and organize relevant resources to deepen your understanding and expand your knowledge base.
Browse courses on
Bayesian Statistics
Show steps
Show steps
Show steps
- Search for online articles, books, and videos on Bayesian Statistics.
- Review the resources and select those that are most informative and relevant.
- Organize the resources into a collection or bibliography.
Discuss Bayesian concepts with peers
Show steps
Engage in discussions with peers to clarify concepts, share insights, and receive feedback.
Browse courses on
Bayesian Statistics
Show steps
Show steps
Show steps
- Join or form a study group with other students in the course.
- Schedule regular meetings to discuss course materials and exchange ideas.
- Prepare for discussions by reviewing materials and preparing questions.
Practice Bayesian Inference
Show steps
Strengthens understanding of Bayesian inference techniques through repeated practice.
Browse courses on
Bayesian Inference
Show steps
Show steps
Show steps
- Solve exercises involving Bayes theorem and posterior probability calculations.
- Use online resources or textbooks for additional problems.
Learn R and OPENBUGS for Bayesian analysis
Show steps
Follow tutorials to gain practical experience in using R and OPENBUGS for Bayesian analysis.
Browse courses on
R
Show steps
Show steps
Show steps
- Find tutorials on R and OPENBUGS.
- Follow the tutorials step-by-step.
- Complete the exercises or projects provided in the tutorials.
Discuss Bayesian Applications
Show steps
Fosters collaboration and exchange of ideas on practical applications of Bayesian Statistics.
Show steps
Show steps
Show steps
- Participate in online forums or discussion groups focused on Bayesian Statistics.
- Share experiences and insights related to Bayesian modeling and analysis.
Bayesian modeling exercises
Show steps
Solve Bayesian modeling exercises to solidify your understanding of the concepts and techniques.
Browse courses on
Bayesian Modeling
Show steps
Show steps
Show steps
- Find practice problems or exercises online or in textbooks.
- Attempt to solve the problems independently.
- Check your solutions against provided solutions or consult with the course instructor.
Create a Bayesian Model
Show steps
Provides hands-on experience in applying Bayesian concepts by creating a custom model.
Browse courses on
Bayesian Analysis
Show steps
Show steps
Show steps
- Define the problem and gather necessary data.
- Choose appropriate priors and likelihood functions.
- Implement the model using a programming language or software.
- Analyze the results and evaluate the model's performance.
Create a Bayesian analysis project
Show steps
Apply your skills to develop a comprehensive Bayesian analysis project, demonstrating your understanding of the concepts.
Browse courses on
Bayesian Statistics
Show steps
Show steps
Show steps
- Identify a suitable dataset and research question.
- Develop a Bayesian model to address the research question.
- Implement the model in R or OPENBUGS and perform the analysis.
- Interpret the results and draw conclusions.
- Present the project in a report or presentation.
Explore Markov Chain Monte Carlo Methods
Show steps
Deepens knowledge of Markov Chain Monte Carlo methods used in Bayesian computations.
Browse courses on
Markov Chain Monte Carlo
Show steps
Show steps
Show steps
- Follow online tutorials or read research papers on MCMC.
- Implement MCMC algorithms in a programming language.
- Analyze the convergence and efficiency of MCMC simulations.
Career center
Learners who complete Applied Bayesian for Analytics will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are in high demand as organizations seek to gain insights from their data in order to make better decisions. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Data Scientist.
Business Analyst
Business Analyst
Business Analysts use data to solve problems and make informed decisions for businesses. They work in a variety of industries, including technology, finance, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Business Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve problems in business and industry. They work in a variety of fields, including manufacturing, transportation, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as an Operations Research Analyst.
Statistician
Statistician
Statisticians use data to solve problems and make informed decisions. They work in a variety of fields, including finance, healthcare, and marketing. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Statistician.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in a variety of industries, including technology, finance, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Software Engineer.
Data Analyst
Data Analyst
Data Analysts use data to solve problems and make informed decisions. They work in a variety of fields, including business, healthcare, and government. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Data Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They work in a variety of fields, including banking, insurance, and asset management. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Financial Analyst.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risk for organizations. They work in a variety of fields, including finance, insurance, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Risk Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematics and statistics to analyze financial data. They work in a variety of fields, including investment banking, hedge funds, and asset management. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Quantitative Analyst.
Actuary
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. They work in a variety of fields, including insurance, pensions, and healthcare. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as an Actuary.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. They work in a variety of fields, including marketing, advertising, and product development. This course in Applied Bayesian for Analytics can help you develop the skills you need to be successful in this career. You will learn how to use Bayesian statistics to analyze data, build models, and make predictions. This course will give you a competitive edge in the job market and help you launch a successful career as a Market Researcher.
For more career information including salaries, visit:
OpenCourser.com/course/wbc2f1/applied
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Applied Bayesian for Analytics.
An essential reference for Bayesian statistics, this book provides comprehensive coverage of Bayesian modeling and analysis techniques. It is commonly used as a textbook for graduate-level courses and serves as a valuable supplement to the course syllabus.
A practical guide to Bayesian data analysis, this book provides hands-on tutorials using R, JAGS, and Stan. It is an excellent resource for gaining practical experience in Bayesian modeling and valuable supplement to the course's empirical component.
Provides a practical guide to Bayesian statistical modeling. It covers various topics such as model building, parameter estimation, and model comparison. It valuable resource for learners seeking a comprehensive understanding of Bayesian modeling.
Provides an accessible introduction to Bayesian statistics, with a focus on understanding the underlying concepts and practical applications. It is written in a clear and engaging style, making it a great choice for learners new to Bayesian statistics.
Provides a hands-on guide to Bayesian analysis using Python. It covers essential concepts and techniques, with a focus on practical applications. It valuable resource for learners seeking to implement Bayesian models in Python.
Provides a comprehensive introduction to Monte Carlo methods, a crucial aspect of Bayesian computation. It covers various techniques and algorithms, making it a valuable reference for learners seeking to understand the theoretical foundations of Bayesian modeling and sampling.
Specifically tailored to the field of biostatistics, this book provides a concise introduction to Bayesian statistics. It covers essential concepts and methods, making it a suitable supplement for learners interested in applying Bayesian statistics in healthcare and medical research.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wbc2f1/applied
Share
Similar courses
Here are nine courses similar to
Applied Bayesian for Analytics.
Bayesian Statistics: Mixture Models
OpenCourser.com/course/pw6r49/bayesian
Bayesian Statistics: Mixture Models
Most relevant
Bayesian Statistics: Time Series Analysis
OpenCourser.com/course/ox8h44/bayesian
Bayesian Statistics: Time Series Analysis
Most relevant
Bayesian Statistics
OpenCourser.com/course/uia8ne/bayesian
Bayesian Statistics
Most relevant
Bayesian Statistics: Techniques and Models
OpenCourser.com/course/1lplko/bayesian
Bayesian Statistics: Techniques and Models
Most relevant
Topics in Applied Econometrics
OpenCourser.com/course/tsifyk/topics
Topics in Applied Econometrics
Most relevant
Sensor Fusion and Non-linear Filtering for Automotive Systems
OpenCourser.com/course/iu3ld3/sensor
Sensor Fusion and Non-linear Filtering for Automotive...
Most relevant
Introduction to Bayesian Statistics Using R
OpenCourser.com/course/5azi8b/introduction
Introduction to Bayesian Statistics Using R
Improving your statistical inferences
OpenCourser.com/course/sbwe9f/improving
Improving your statistical inferences
Advanced Bayesian Statistics Using R
OpenCourser.com/course/9hvbgg/advanced
Advanced Bayesian Statistics Using R
Effort
2 to 3 hours per week for 6 weeks
Level
Introductory
Via
edX
Institution
Indian Institute of Management Bangalore
Instructor
Pulak Ghosh
Language
English
Transcript
English
Good to know
Builds a strong foundation for beginners in Bayesian statistics
Provides hands-on experience with Bayesian modeling using R and OPENBUGS
Develops an understanding of the Bayesian approach to data analysis and probability estimation
Useful for learners interested in applying Bayesian statistics to real-world problems
Taught by Pulak Ghosh, who is recognized for his work in Bayesian statistics
Explored through a mix of theoretical and empirical components for a comprehensive understanding
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser