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Dr. Srijith Rajamohan
The objective of this course is to introduce Markov Chain Monte Carlo Methods for Bayesian modeling and inference, The attendees will start off by learning the the basics of Monte Carlo methods. This will be augmented by hands-on examples in Python that will be used to illustrate how these algorithms work. This will be the second course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Read more
The objective of this course is to introduce Markov Chain Monte Carlo Methods for Bayesian modeling and inference, The attendees will start off by learning the the basics of Monte Carlo methods. This will be augmented by hands-on examples in Python that will be used to illustrate how these algorithms work. This will be the second course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Read more
The objective of this course is to introduce Markov Chain Monte Carlo Methods for Bayesian modeling and inference, The attendees will start off by learning the the basics of Monte Carlo methods. This will be augmented by hands-on examples in Python that will be used to illustrate how these algorithms work. This will be the second course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
The instructor for this course will be Dr. Srijith Rajamohan.
Register for this course and see more details by visiting:
OpenCourser.com/course/yjfzaz/bayesian
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What's inside
Syllabus
Topics in Model Performance
This module gives an overview of topics related to assessing the quality of models. While some of these metrics may be familiar to those with a Machine Learning background, the goal is to bring awareness to the concepts rooted in Information Theory. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/BayesianInference.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html
Read more
Syllabus
Topics in Model Performance
This module gives an overview of topics related to assessing the quality of models. While some of these metrics may be familiar to those with a Machine Learning background, the goal is to bring awareness to the concepts rooted in Information Theory. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/BayesianInference.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html
Read more
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Well-suited for those seeking an introduction to Bayesian modeling and inference using Markov Chain Monte Carlo methods
Taught by Dr. Srijith Rajamohan, an experienced instructor in computational statistics
Utilizes Python and Jupyter notebooks for hands-on examples, making the concepts more accessible
Builds a strong foundation for understanding the basics of Monte Carlo methods and their application in Bayesian modeling
Covers a range of topics in Bayesian inference, including model performance, Metropolis algorithms, Gibbs sampling, and Hamiltonian Monte Carlo
May require prior knowledge of Python and Jupyter notebooks for optimal learning
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Reviews summary
Practical mcmc with python and pymc3
According to learners, this course provides a solid foundation in Bayesian inference and Markov Chain Monte Carlo (MCMC) methods. Students generally find the lectures clear and well-explained, particularly appreciating the instructor's ability to demystify complex topics. The inclusion of practical Python examples and Jupyter notebooks with PyMC3 usage is frequently highlighted as a major strength, enabling hands-on application of the learned concepts. However, a consistent note of caution is that the course demands a strong mathematical and statistical background, potentially posing a challenge for those without the necessary prerequisites. Some also note that while comprehensive, the coverage of certain complex algorithms like Hamiltonian Monte Carlo is more high-level.
Recent updates have improved clarity and notebook stability.
"I took this course recently, and the updated notebooks are much smoother to run compared to what I heard from earlier reviews."
"The instructor seems to have addressed some of the earlier feedback regarding the explanations; they feel clearer now."
"It's great to see the course being maintained and improved based on student suggestions; it really makes a difference."
The course offers valuable hands-on coding exercises using PyMC3.
"The Jupyter notebooks are fantastic; they truly solidify the theoretical concepts with practical PyMC3 applications."
"I really appreciated the hands-on approach and being able to work through the MCMC algorithms in Python."
"The practical exercises with PyMC3 were the highlight; they allowed me to apply what I learned immediately."
The instructor effectively clarifies complex Bayesian and MCMC topics.
"Dr. Rajamohan does an excellent job of explaining complex concepts like Metropolis-Hastings in an understandable way."
"I found the lectures provided a very clear understanding of the underlying theory of MCMC methods."
"The way the instructor broke down the algorithms made them much easier to grasp than from textbooks alone."
Some advanced topics are covered more superficially than others.
"The introduction to HMC felt a bit rushed, I would have liked more in-depth explanations and examples there."
"While it lays a good foundation, I felt some algorithms could have been explored with more detail for practical implementation."
"It's a good overview, but don't expect to become an expert on every MCMC variant without further study."
Learners should possess a strong math and statistics background.
"Be warned, you definitely need a solid foundation in calculus, linear algebra, and probability theory to truly benefit."
"While the course tries to be gentle, I found it quite challenging without a strong background in advanced statistics."
"This course is not for the faint of heart; it assumes a level of mathematical maturity that some beginners might lack."
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 Inference with MCMC with these
activities:
Review general statistics
Show steps
Start the course with a solid foundation in statistics.
Browse courses on
Statistics
Show steps
-
Review foundational statistics concepts, such as mean, median, mode, and standard deviation.
-
Practice applying statistical concepts to real-world data.
Organize your notes, assignments, quizzes, and exams
Show steps
Stay organized and improve your ability to recall information.
Show steps
-
Gather all of your course materials, including notes, assignments, quizzes, and exams.
-
Organize the materials by topic or module.
Work through practice problems on probability distributions
Show steps
Develop a strong understanding of probability distributions and their applications.
Browse courses on
Probability
Show steps
-
Solve practice problems on different types of probability distributions, such as binomial, normal, and Poisson.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore tutorials on Bayesian modeling with Python
Show steps
Gain hands-on experience with Bayesian modeling using Python.
Browse courses on
Bayesian Modeling
Show steps
-
Find tutorials on Bayesian modeling with Python.
-
Follow the tutorials and implement the techniques in Python.
Create a cheat sheet or infographic on Markov chains
Show steps
Deepen your understanding of Markov chains and their properties.
Browse courses on
Markov Chains
Show steps
-
Gather key concepts and information about Markov chains.
-
Organize and present the information in a clear and visually appealing manner.
Start a project to apply MCMC methods to a real-world problem
Show steps
Apply your knowledge of MCMC to solve real-world problems and gain practical experience.
Browse courses on
Monte Carlo Methods
Show steps
-
Identify a real-world problem that can be addressed using MCMC methods.
-
Gather and prepare data for the problem.
-
Implement MCMC algorithms to solve the problem.
-
Analyze the results and draw conclusions.
Participate in hackathons or competitions that focus on Bayesian modeling
Show steps
Challenge yourself and test your skills in a competitive environment.
Browse courses on
Bayesian Modeling
Show steps
-
Find hackathons or competitions that focus on Bayesian modeling.
-
Form a team or work individually.
-
Develop a solution to the competition problem.
-
Submit your solution and compete for prizes.
Mentor other students who are learning about Bayesian modeling
Show steps
Enhance your understanding by sharing your knowledge and helping others.
Browse courses on
Bayesian Modeling
Show steps
-
Identify opportunities to mentor other students, such as joining online forums or tutoring.
-
Provide guidance and support to students who are learning about Bayesian modeling.
Review general statistics
Show steps
Start the course with a solid foundation in statistics.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review foundational statistics concepts, such as mean, median, mode, and standard deviation.
- Practice applying statistical concepts to real-world data.
Organize your notes, assignments, quizzes, and exams
Show steps
Stay organized and improve your ability to recall information.
Show steps
Show steps
Show steps
- Gather all of your course materials, including notes, assignments, quizzes, and exams.
- Organize the materials by topic or module.
Work through practice problems on probability distributions
Show steps
Develop a strong understanding of probability distributions and their applications.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Solve practice problems on different types of probability distributions, such as binomial, normal, and Poisson.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore tutorials on Bayesian modeling with Python
Show steps
Gain hands-on experience with Bayesian modeling using Python.
Browse courses on
Bayesian Modeling
Show steps
Show steps
Show steps
- Find tutorials on Bayesian modeling with Python.
- Follow the tutorials and implement the techniques in Python.
Create a cheat sheet or infographic on Markov chains
Show steps
Deepen your understanding of Markov chains and their properties.
Browse courses on
Markov Chains
Show steps
Show steps
Show steps
- Gather key concepts and information about Markov chains.
- Organize and present the information in a clear and visually appealing manner.
Start a project to apply MCMC methods to a real-world problem
Show steps
Apply your knowledge of MCMC to solve real-world problems and gain practical experience.
Browse courses on
Monte Carlo Methods
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using MCMC methods.
- Gather and prepare data for the problem.
- Implement MCMC algorithms to solve the problem.
- Analyze the results and draw conclusions.
Participate in hackathons or competitions that focus on Bayesian modeling
Show steps
Challenge yourself and test your skills in a competitive environment.
Browse courses on
Bayesian Modeling
Show steps
Show steps
Show steps
- Find hackathons or competitions that focus on Bayesian modeling.
- Form a team or work individually.
- Develop a solution to the competition problem.
- Submit your solution and compete for prizes.
Mentor other students who are learning about Bayesian modeling
Show steps
Enhance your understanding by sharing your knowledge and helping others.
Browse courses on
Bayesian Modeling
Show steps
Show steps
Show steps
- Identify opportunities to mentor other students, such as joining online forums or tutoring.
- Provide guidance and support to students who are learning about Bayesian modeling.
Career center
Learners who complete Bayesian Inference with MCMC will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. They work with large datasets and use their expertise in machine learning algorithms and techniques to solve real-world problems, such as image recognition, natural language processing, and speech recognition. Markov Chain Monte Carlo (MCMC) algorithms are often used in machine learning, especially for Bayesian modeling, so this course can help you build a strong foundation for a career in this field. The course's focus on Python and Jupyter notebooks also aligns with the tools and technologies used by practicing Machine Learning Engineers.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare, finance, and marketing. Markov Chain Monte Carlo (MCMC) algorithms are often used in statistics, especially for Bayesian modeling, so this course can help you build a strong foundation for a career in this field. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are widely used by practicing Statisticians.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts develop and implement mathematical models to analyze financial data and make investment decisions. They use statistical methods, machine learning, and econometrics to identify trends, patterns, and relationships in data. Markov Chain Monte Carlo (MCMC) algorithms are often used in quantitative finance, so this course can help you build a strong foundation for a career in this field. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are widely used in the financial industry.
Data Scientist
Data Scientist
Data Scientists use statistical methods, algorithms, and machine learning techniques to extract knowledge and insights from data. They work with large and complex datasets, and use their expertise to solve real-world problems, such as predicting customer behavior, optimizing marketing campaigns, and detecting fraud. Many Data Scientists work with Markov Chain Monte Carlo (MCMC) algorithms, especially in the context of Bayesian statistics, so this course can help you build the necessary foundation to succeed in the field. The course's emphasis on Python and Jupyter notebooks also aligns with the tools and technologies used by practicing Data Scientists.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investments. They use financial data and analysis to help individuals and organizations make informed investment decisions. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in financial analysis, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the financial industry.
Investment Analyst
Investment Analyst
Investment Analysts evaluate and make recommendations on investments. They use financial data and analysis to help individuals and organizations make informed investment decisions. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in investment analysis, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the financial industry.
Portfolio Manager
Portfolio Manager
Portfolio Managers manage and invest money on behalf of clients. They use financial data and analysis to make investment decisions and develop investment strategies. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in portfolio management, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the financial industry.
Hedge Fund Manager
Hedge Fund Manager
Hedge Fund Managers manage and invest money on behalf of clients. They use financial data and analysis to make investment decisions and develop investment strategies. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in hedge fund management, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the financial industry.
Insurance Analyst
Insurance Analyst
Insurance Analysts evaluate and make recommendations on insurance policies. They use financial data and analysis to help individuals and organizations understand and mitigate risk. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in insurance analysis, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the insurance industry.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts evaluate and make recommendations on healthcare policies and programs. They use data and analysis to help healthcare providers and insurers understand and improve the quality and efficiency of healthcare services. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in healthcare analysis, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the healthcare industry.
Research Analyst
Research Analyst
Research Analysts conduct research and make recommendations on investments, companies, and industries. They use financial data and analysis to help individuals and organizations make informed investment decisions. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in research analysis, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the financial industry.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends, patterns, and insights. They use statistical methods, data visualization, and machine learning to communicate their findings to stakeholders. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in data analysis, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are widely used by Data Analysts.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. They work in a variety of fields, including insurance, finance, and healthcare. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in actuarial science, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the actuarial profession.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks. They work in a variety of fields, including finance, insurance, and healthcare. Markov Chain Monte Carlo (MCMC) algorithms are sometimes used in risk analysis, especially for Bayesian modeling, so this course may be helpful for those who want to specialize in this area. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the risk management profession.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. They work with stakeholders to identify problems, develop solutions, and communicate their findings. Markov Chain Monte Carlo (MCMC) algorithms are not commonly used in business analysis, but this course may be helpful for those who want to develop a deeper understanding of statistical methods and modeling. The course's focus on Python and Jupyter notebooks is also relevant, as these tools are becoming increasingly popular in the business world.
For more career information including salaries, visit:
OpenCourser.com/course/yjfzaz/bayesian
Reading list
We've selected nine 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 Inference with MCMC.
Provides a comprehensive overview of Bayesian data analysis, covering both theoretical and practical aspects. It valuable resource for anyone interested in learning more about Bayesian methods.
Provides a comprehensive overview of Monte Carlo statistical methods, including Markov chain Monte Carlo (MCMC) methods. It valuable resource for anyone interested in learning more about MCMC methods.
Provides a gentle introduction to Bayesian statistics. It valuable resource for anyone interested in learning more about Bayesian methods, especially those who are new to the field.
Provides a theoretical introduction to Markov chain Monte Carlo (MCMC) methods. It valuable resource for anyone interested in learning more about the theoretical underpinnings of MCMC methods.
Provides a practical introduction to Bayesian analysis for social scientists. It valuable resource for anyone interested in learning more about Bayesian methods, especially those who are new to the field.
Provides a gentle introduction to Bayesian statistics. It valuable resource for anyone interested in learning more about Bayesian methods, especially those who are new to the field.
Provides a practical introduction to Bayesian data analysis. It valuable resource for anyone interested in learning more about Bayesian methods, especially those who are new to the field.
Provides a practical introduction to Bayesian computation using the R programming language. It valuable resource for anyone interested in learning more about Bayesian methods, especially those who are new to the field.
Provides a practical introduction to Bayesian analysis using the Python programming language. It valuable resource for anyone interested in learning more about Bayesian methods, especially those who are new to the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yjfzaz/bayesian
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This course belongs to a
collection
called
Introduction to Computational Statistics for Data Scientists. It's comprised of three courses:
- Introduction to Bayesian Statistics
- Bayesian Inference with MCMC (viewing)
- Introduction to PyMC3 for Bayesian Modeling and Inference
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