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Abel Rodriguez
Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application.
Read more
Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application.
Read more
Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application.
Some exercises require the use of R, a freely-available statistical software package. A brief tutorial is provided, but we encourage you to take advantage of the many other resources online for learning R if you are interested.
This is an intermediate-level course, and it was designed to be the third in UC Santa Cruz's series on Bayesian statistics, after Herbie Lee's "Bayesian Statistics: From Concept to Data Analysis" and Matthew Heiner's "Bayesian Statistics: Techniques and Models." To succeed in the course, you should have some knowledge of and comfort with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
Register for this course and see more details by visiting:
OpenCourser.com/course/pw6r49/bayesian
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What's inside
Syllabus
Basic concepts on Mixture Models
This module defines mixture models, discusses its properties, and develops the likelihood function for a random sample from a mixture model that will be the basis for statistical learning.
Read more
Syllabus
Basic concepts on Mixture Models
This module defines mixture models, discusses its properties, and develops the likelihood function for a random sample from a mixture model that will be the basis for statistical learning.
Read more
Maximum likelihood estimation for Mixture Models
Bayesian estimation for Mixture Models
Applications of Mixture Models
Practical considerations
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops the likelihood function for a random sample from a mixture model that will be the basis for statistical learning
Provides a brief tutorial to get started with the necessary statistical software package, R
Taught by Abel Rodriguez, an expert in the field of Bayesian statistics
Encourages learners to engage in discussions and complete peer-reviewed assignments to foster active participation
Requires prior knowledge of calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation
Introduces learners to an important class of statistical models, Mixture Models
Save this course
Save Bayesian Statistics: Mixture Models to your list so you can find it easily later:
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Reviews summary
Challenging but comprehensive bayesian mixture models
Learners say this is a comprehensive and well-presented course on Bayesian Mixture Models. It features a balance of theory and practice, with engaging video lectures and in-depth programming assignments. The course provides a strong foundation in the mathematics behind Bayesian mixture models and explores their applications in various fields. However, it is important to note that the course is advanced and assumes some prior knowledge in statistics and computer science.
Includes practical examples and assignments.
"Examples of using these models (density estimation, (semi-) supervised classification and clustering) are also given."
"If the homework is done carefully, most of the knowledge points involved in this course should be mastered."
"The code assignments of the course also let me understand how these algorithms are implemented, and also let me see its specific practical applications."
Covers advanced mathematical concepts.
"A solid foundation in the mathematics of Bayesian mixture models is as important as the application of the models used later in the specialization."
"Initially thought that the programming assignments would be difficult but after following videos and instructions, I started gaining confidence."
"This is a course that combines principles and practice."
Assignments require peer review, which can be challenging.
"The only downside is that the waiting time for peer review is too long."
"The reason I rate 4 star is that there are a lot of peer review tasks, which is something that I don't like at all."
"Even they are optional (honor), these assignments could have been designed as automatically graded programming ones."
Requires strong background in statistics and computer science.
"The course is too theoretical and technical."
"Unfortunately, there are certain disadvantages in the course."
"I felt that this course was a bit difficult."
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 Bayesian Statistics: Mixture Models with these
activities:
Create a Course Notes Repository
Show steps
Organize and expand upon course materials to enhance your understanding and prepare for assessments.
Show steps
-
Review lecture videos
-
Summarize key points
-
Create a structured outline
-
Compile additional resources
Review Probability and Statistics Book
Show steps
Review basic concepts in probability and statistics to strengthen your foundational knowledge and prepare for this course.
View
Probability & Statistics for Engineers &...
on Amazon
Show steps
-
Read chapters 1-3
-
Complete end-of-chapter exercises
-
Take practice quizzes online
Tutorial: Introduction to R for Bayesian Analysis
Show steps
Gain familiarity with the R software package, which will be used extensively in the course for data analysis and modeling.
Browse courses on
R
Show steps
-
Follow the tutorial steps
-
Complete the practice exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group
Show steps
Collaborate with peers to discuss course concepts, solve problems, and support each other's learning.
Show steps
-
Find a study group or create one
-
Meet regularly to discuss course material
-
Work together on assignments
Practice Maximum Likelihood Estimation
Show steps
Reinforce your understanding of maximum likelihood estimation, a key concept in Bayesian statistics.
Browse courses on
Maximum Likelihood Estimation
Show steps
-
Solve practice problems
-
Review solutions
-
Repeat until proficient
Create a Presentation on Bayesian Statistics
Show steps
Summarize and present the key concepts of Bayesian statistics to reinforce your understanding and share your knowledge.
Browse courses on
Bayesian Statistics
Show steps
-
Outline the main topics
-
Gather relevant materials
-
Create visual aids and slides
-
Practice delivering the presentation
Create a Bayesian Network Model
Show steps
Apply your knowledge of Bayesian networks by creating a model for a real-world problem.
Browse courses on
Bayesian Modeling
Show steps
-
Identify a suitable problem
-
Construct the network structure
-
Estimate the conditional probability tables
-
Validate the model
Develop a Bayesian Model for a Research Project
Show steps
Apply your skills in Bayesian statistics to a real-world research project and make valuable contributions to your field.
Show steps
-
Identify a research question
-
Collect and prepare data
-
Build and evaluate the Bayesian model
-
Interpret the results
-
Write a research paper or present your findings
Create a Course Notes Repository
Show steps
Organize and expand upon course materials to enhance your understanding and prepare for assessments.
Show steps
Show steps
Show steps
- Review lecture videos
- Summarize key points
- Create a structured outline
- Compile additional resources
Review Probability and Statistics Book
Show steps
Review basic concepts in probability and statistics to strengthen your foundational knowledge and prepare for this course.
View
Probability & Statistics for Engineers &...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3
- Complete end-of-chapter exercises
- Take practice quizzes online
Tutorial: Introduction to R for Bayesian Analysis
Show steps
Gain familiarity with the R software package, which will be used extensively in the course for data analysis and modeling.
Browse courses on
R
Show steps
Show steps
Show steps
- Follow the tutorial steps
- Complete the practice exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group
Show steps
Collaborate with peers to discuss course concepts, solve problems, and support each other's learning.
Show steps
Show steps
Show steps
- Find a study group or create one
- Meet regularly to discuss course material
- Work together on assignments
Practice Maximum Likelihood Estimation
Show steps
Reinforce your understanding of maximum likelihood estimation, a key concept in Bayesian statistics.
Browse courses on
Maximum Likelihood Estimation
Show steps
Show steps
Show steps
- Solve practice problems
- Review solutions
- Repeat until proficient
Create a Presentation on Bayesian Statistics
Show steps
Summarize and present the key concepts of Bayesian statistics to reinforce your understanding and share your knowledge.
Browse courses on
Bayesian Statistics
Show steps
Show steps
Show steps
- Outline the main topics
- Gather relevant materials
- Create visual aids and slides
- Practice delivering the presentation
Create a Bayesian Network Model
Show steps
Apply your knowledge of Bayesian networks by creating a model for a real-world problem.
Browse courses on
Bayesian Modeling
Show steps
Show steps
Show steps
- Identify a suitable problem
- Construct the network structure
- Estimate the conditional probability tables
- Validate the model
Develop a Bayesian Model for a Research Project
Show steps
Apply your skills in Bayesian statistics to a real-world research project and make valuable contributions to your field.
Show steps
Show steps
Show steps
- Identify a research question
- Collect and prepare data
- Build and evaluate the Bayesian model
- Interpret the results
- Write a research paper or present your findings
Career center
Learners who complete Bayesian Statistics: Mixture Models will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians have a theoretical grounding in Bayesian methods, probability, and statistical learning. A course in Bayesian Statistics: Mixture Models would provide a good foundation in these concepts, helping Statisticians to develop and apply statistical models to real-world problems. This course would also be helpful for Statisticians who use R for data analysis, as it provides a brief tutorial on the software.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning algorithms to solve real-world problems. Bayesian Statistics: Mixture Models would provide a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in the field of machine learning.
Data Scientist
Data Scientist
Data Scientists use statistical models to extract insights from data. Bayesian Statistics: Mixture Models would provide a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in the field of data science.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical models to solve problems in business and industry. Bayesian Statistics: Mixture Models would provide a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in the field of operations research.
Market Researcher
Market Researcher
Market Researchers use statistical models to analyze market data and make marketing decisions. Bayesian Statistics: Mixture Models would provide a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in the field of market research.
Biostatistician
Biostatistician
Biostatisticians use statistical models to analyze biological data and make medical decisions. Bayesian Statistics: Mixture Models would provide a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in the field of biostatistics.
Epidemiologist
Epidemiologist
Epidemiologists use statistical models to investigate the causes and spread of disease. Bayesian Statistics: Mixture Models would provide a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in the field of epidemiology.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical models to analyze financial data and make investment decisions. Bayesian Statistics: Mixture Models would provide a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in the field of quantitative finance.
Risk Manager
Risk Manager
Risk Managers use statistical models to assess risk and make financial decisions. Bayesian Statistics: Mixture Models would provide a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in the field of risk management.
Actuary
Actuary
Actuaries use statistical models to assess risk and make financial decisions. Bayesian Statistics: Mixture Models would provide a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in the field of actuarial science.
Teacher
Teacher
Teachers of statistics and data science may find Bayesian Statistics: Mixture Models to be a helpful resource. The course provides a good foundation in the theory and application of Bayesian statistical models, which are becoming increasingly popular in these fields.
Financial Analyst
Financial Analyst
Financial Analysts use statistical models to analyze financial data and make investment decisions. Bayesian Statistics: Mixture Models may be a useful resource for Financial Analysts who want to learn more about Bayesian statistical methods.
Business Analyst
Business Analyst
Business Analysts use statistical models to analyze data and solve business problems. Bayesian Statistics: Mixture Models may be a useful resource for Business Analysts who want to learn more about Bayesian statistical methods.
Software Engineer
Software Engineer
Software Engineers who work with data may find Bayesian Statistics: Mixture Models to be a helpful resource. The course provides a good foundation in the theory and application of Bayesian statistical models, which are used in a variety of software applications.
Data Analyst
Data Analyst
Data Analysts use statistical models to analyze data and extract insights. Bayesian Statistics: Mixture Models may be a useful resource for Data Analysts who want to learn more about Bayesian statistical methods.
For more career information including salaries, visit:
OpenCourser.com/course/pw6r49/bayesian
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
Bayesian Statistics: Mixture Models.
Provides an updated and comprehensive overview of statistical learning, including mixture models. It valuable resource for students and practitioners who want to learn more about these topics.
Comprehensive introduction to Bayesian statistics and provides a detailed overview of mixture models. It valuable reference for students and practitioners who want to learn more about the theory and applications of mixture models.
Provides a theoretical foundation for mixture models and discusses various methods for fitting and evaluating mixture models. It valuable resource for students and researchers who want to gain a deeper understanding of the theory behind mixture models.
Provides a unified approach to latent variable models and factor analysis, including mixture models. It valuable resource for students and researchers who want to gain a deeper understanding of these models.
Provides a comprehensive overview of pattern recognition and machine learning, including mixture models. It valuable resource for students and practitioners who want to learn more about these topics.
Provides a comprehensive overview of statistical learning, including mixture models. It valuable resource for students and practitioners who want to learn more about these topics.
Save
Provides a Bayesian and optimization perspective on machine learning, including mixture models. It valuable resource for students and practitioners who want to learn more about these topics from a different perspective.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pw6r49/bayesian
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Level
Intermediate
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Institution
University of California, Santa Cruz
Instructor
Abel Rodriguez
Language
English
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This course belongs to a
collection
called
Bayesian Statistics. It's comprised of five courses:
- Bayesian Statistics: Mixture Models (viewing)
- Bayesian Statistics: Time Series Analysis
- Bayesian Statistics: Techniques and Models
- Bayesian Statistics: From Concept to Data Analysis
- Bayesian Statistics: Capstone Project
Good to know
Develops the likelihood function for a random sample from a mixture model that will be the basis for statistical learning
Provides a brief tutorial to get started with the necessary statistical software package, R
Taught by Abel Rodriguez, an expert in the field of Bayesian statistics
Encourages learners to engage in discussions and complete peer-reviewed assignments to foster active participation
Requires prior knowledge of calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation
Introduces learners to an important class of statistical models, Mixture Models
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