Bayesian Inference with MCMC from Coursera

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.

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

The Metropolis Algorithms for MCMC

This module serves as a gentle introduction to Markov-Chain Monte Carlo methods. The general idea behind Markov chains are presented along with their role in sampling from distributions. The Metropolis and Metropolis-Hastings algorithms are introduced and implemented in Python to help illustrate their details. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Gibbs Sampling and Hamiltonian Monte Carlo Algorithms

This module is a continuation of module 2 and introduces Gibbs sampling and the Hamiltonian Monte Carlo (HMC) algorithms for inferring distributions. The Gibbs sampler algorithm is illustrated in detail, while the HMC receives a more high-level treatment due to the complexity of the algorithm. Finally, some of the properties of MCMC algorithms are presented to set the stage for Course 3 which uses the popular probabilistic framework PyMC3. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html#gibbs-sampling. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

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

Reviews summary

Bayesian statistics mcmc

According to students, Bayesian Inference with MCMC is a course covering Bayesian statistics and Markov Chain Monte Carlo (MCMC) simulation techniques. Learners report positive aspects of the course, such as its coding assignments and short duration. On the other hand, students also have concerns. Many mention the lack of a consistent grading rubric and the poor quality of the lectures. Several students also complain about the lecturer's delivery.

Course is well-paced.

"The class is an ok 8 hour refresher for someone who already knows Bayesian statistics and MCMC."

Course includes hands-on assignments.

"The coding assignments are do-able."

No answer key provided for assignments.

"The course content does not include an example solution to the ungraded coding homework assignments."

Lectures are subpar.

"The lectures are fair to poor, with notes that neither visually convey the intuition of each method nor the mathematical details."

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

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Start the course with a solid foundation in statistics.

Browse courses on Statistics

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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

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Stay organized and improve your ability to recall information.

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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

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Develop a strong understanding of probability distributions and their applications.

Browse courses on Probability

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Solve practice problems on different types of probability distributions, such as binomial, normal, and Poisson.

Five other activities

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Explore tutorials on Bayesian modeling with Python

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Gain hands-on experience with Bayesian modeling using Python.

Browse courses on Bayesian Modeling

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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

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Deepen your understanding of Markov chains and their properties.

Browse courses on Markov Chains

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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

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Apply your knowledge of MCMC to solve real-world problems and gain practical experience.

Browse courses on Monte Carlo Methods

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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

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Challenge yourself and test your skills in a competitive environment.

Browse courses on Bayesian Modeling

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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

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Enhance your understanding by sharing your knowledge and helping others.

Browse courses on Bayesian Modeling

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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:

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Healthcare 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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Risk 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Portfolio Manager

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.

See salaries and explore the career path for Investment 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.

See salaries and explore the career path for Insurance 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.

See salaries and explore the career path for Research Analyst

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.

See salaries and explore the career path for Hedge Fund Manager

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.

See salaries and explore the career path for Business Analyst