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Gordon Enderle
Actuarial work involves modeling future contingent events that are uncertain in terms of timing, frequency and severity. Understanding the central tendency of a contingency and its possible distribution is critical for an actuary to help individuals and institutions manage risk. This course starts with a brief discussion of data analysis, focusing on the central tendency and distribution of data. Introductory probability concepts are introduced next. The course concludes with a study of discrete probability models with an eye toward actuarial application.
Register for this course and see more details by visiting:
OpenCourser.com/course/hd0i0q/probability
What's inside
Learning objectives
- Those enrolled in probability for actuaries: introduction to discrete distribution will learn to:
- Describe basic data types
- Describe the central tendency measures of datasets: mean, median and mode
- Describe the dispersion measures of datasets: range, percentiles and variance
- Describe basic probability concepts including sample space, events and set operations
- Calculate probabilities for simple discrete events
- Differentiate between a discrete and continuous random variable
- Describe bayes theorem, conditional probability, law of total probability and statistical independence
- Describe and use a probability mass function, probability density function and cumulative distribution function
- Describe and calculate the mathematical expectation of a random variable
- Describe the features and application of the following discrete distributions: uniform, binomial, poisson
- Calculate probabilities for random variables governed by a uniform, binomial or poisson distribution
- Describe the features and application of the following discrete distributions: geometric, negative binomial
- Calculate probabilities for random variables governed by a geometric or negative binomial distribution
- Those enrolled in probability for actuaries: introduction to discrete distribution will learn to:
- Describe basic data types
- Describe the central tendency measures of datasets: mean, median and mode
- Describe the dispersion measures of datasets: range, percentiles and variance
- Describe basic probability concepts including sample space, events and set operations
- Calculate probabilities for simple discrete events
- Differentiate between a discrete and continuous random variable
- Describe bayes theorem, conditional probability, law of total probability and statistical independence
- Describe and use a probability mass function, probability density function and cumulative distribution function
- Describe and calculate the mathematical expectation of a random variable
- Describe the features and application of the following discrete distributions: uniform, binomial, poisson
- Calculate probabilities for random variables governed by a uniform, binomial or poisson distribution
- Describe the features and application of the following discrete distributions: geometric, negative binomial
- Calculate probabilities for random variables governed by a geometric or negative binomial distribution
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers math and statistical theory that's commonly found in actuarial work
Offers a mix of text-based lessons, videos, and interactive exercises
Provides a strong grasp of the foundational concepts of probability theory
Taught by seasoned instructors who have made significant contributions to the field of actuarial science
Employs a variety of real-world examples to illustrate the practical applications of probability theory in actuarial work
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Save Probability for Actuaries: Introduction to Discrete Distribution to your list so you can find it easily later:
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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 Probability for Actuaries: Introduction to Discrete Distribution with these
activities:
Organize and Review Course Materials
Show steps
Organizing and reviewing your course materials will help you improve your understanding of the concepts covered in the course and identify areas where you need additional support.
Browse courses on
Probability
Show steps
-
Create a system for organizing your notes, assignments, and other course materials
-
Review your materials regularly
-
Make use of online resources to supplement your understanding of the course material
Review: Introduction to Probability by J. Laurie Snell
Show steps
Reviewing this introductory probability textbook will help you expand your knowledge of the course's fundamental concepts.
View
Denumerable Markov Chains
on Amazon
Show steps
-
Solve the end of chapter questions
-
Summarize the key concepts
-
Read the first chapter
Review Core Statistical Concepts
Show steps
Refreshing your understanding of core statistical concepts will help you better grasp the probabilities and distributions covered by the course.
Browse courses on
Probability
Show steps
-
Review the central tendency measures
-
Review the dispersion measures
-
Review the basic probability concepts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Calculating Probabilities
Show steps
Practicing calculating probabilities will help you gain proficiency in this essential skill for actuarial work.
Browse courses on
Probability
Show steps
-
Solve 20 practice problems
-
Review your answers
Follow a Guided Tutorial on Discrete Probability Distributions
Show steps
Following a guided tutorial on discrete probability distributions will provide you with step-by-step instructions on how to use these distributions to solve actuarial problems.
Browse courses on
Probability
Show steps
-
Watch the tutorial videos
-
Complete the practice exercises
Participate in a Peer Study Group
Show steps
Participating in a peer study group will allow you to discuss the course material with other students and gain different perspectives on the concepts.
Browse courses on
Probability
Show steps
-
Join a study group
-
Meet regularly to discuss the course material
Create a Cheat Sheet on Discrete Probability Distributions
Show steps
Creating a cheat sheet on discrete probability distributions will help you summarize the key concepts and formulas in a concise and accessible format.
Browse courses on
Probability
Show steps
-
List the different discrete probability distributions
-
Write down the key formulas for each distribution
Attend a Workshop on Actuarial Probability
Show steps
Attending a workshop on actuarial probability will provide you with an opportunity to learn from experts in the field and gain insights into the practical applications of probability to actuarial work.
Browse courses on
Probability
Show steps
-
Find a workshop that aligns with your interests
-
Register for the workshop
-
Attend the workshop
Organize and Review Course Materials
Show steps
Organizing and reviewing your course materials will help you improve your understanding of the concepts covered in the course and identify areas where you need additional support.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Create a system for organizing your notes, assignments, and other course materials
- Review your materials regularly
- Make use of online resources to supplement your understanding of the course material
Review: Introduction to Probability by J. Laurie Snell
Show steps
Reviewing this introductory probability textbook will help you expand your knowledge of the course's fundamental concepts.
View
Denumerable Markov Chains
on Amazon
Show steps
Show steps
Show steps
- Solve the end of chapter questions
- Summarize the key concepts
- Read the first chapter
Review Core Statistical Concepts
Show steps
Refreshing your understanding of core statistical concepts will help you better grasp the probabilities and distributions covered by the course.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review the central tendency measures
- Review the dispersion measures
- Review the basic probability concepts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Calculating Probabilities
Show steps
Practicing calculating probabilities will help you gain proficiency in this essential skill for actuarial work.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Solve 20 practice problems
- Review your answers
Follow a Guided Tutorial on Discrete Probability Distributions
Show steps
Following a guided tutorial on discrete probability distributions will provide you with step-by-step instructions on how to use these distributions to solve actuarial problems.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Watch the tutorial videos
- Complete the practice exercises
Participate in a Peer Study Group
Show steps
Participating in a peer study group will allow you to discuss the course material with other students and gain different perspectives on the concepts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Join a study group
- Meet regularly to discuss the course material
Create a Cheat Sheet on Discrete Probability Distributions
Show steps
Creating a cheat sheet on discrete probability distributions will help you summarize the key concepts and formulas in a concise and accessible format.
Browse courses on
Probability
Show steps
Show steps
Show steps
- List the different discrete probability distributions
- Write down the key formulas for each distribution
Attend a Workshop on Actuarial Probability
Show steps
Attending a workshop on actuarial probability will provide you with an opportunity to learn from experts in the field and gain insights into the practical applications of probability to actuarial work.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Find a workshop that aligns with your interests
- Register for the workshop
- Attend the workshop
Career center
Learners who complete Probability for Actuaries: Introduction to Discrete Distribution will develop knowledge and skills
that may be useful to these careers:
Actuary
Actuary
Actuaries are responsible for assessing and managing financial risk. They use mathematical and statistical techniques to analyze data and develop models that can predict future events. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for actuaries. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for actuaries who want to be able to accurately assess and manage risk.
Insurance Underwriter
Insurance Underwriter
Insurance underwriters assess and manage risk for insurance companies. They use mathematical and statistical techniques to analyze data and develop models that can predict the likelihood of an event occurring. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for insurance underwriters. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for insurance underwriters who want to be able to accurately assess and manage risk.
Financial Analyst
Financial Analyst
Financial analysts provide investment advice to individuals and institutions. They use mathematical and statistical techniques to analyze data and develop models that can predict the performance of financial investments. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for financial analysts. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for financial analysts who want to be able to accurately assess and manage risk.
Data Analyst
Data Analyst
Data analysts use mathematical and statistical techniques to analyze data and develop models that can be used to make decisions. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for data analysts. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for data analysts who want to be able to accurately analyze data and develop models.
Risk Manager
Risk Manager
Risk managers assess and manage risk for organizations. They use mathematical and statistical techniques to analyze data and develop models that can predict the likelihood of an event occurring. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for risk managers. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for risk managers who want to be able to accurately assess and manage risk.
Business Analyst
Business Analyst
Business analysts use mathematical and statistical techniques to analyze data and develop models that can be used to make decisions. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for business analysts. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for business analysts who want to be able to accurately analyze data and develop models.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, and interpret data. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for statisticians. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for statisticians who want to be able to accurately collect, analyze, and interpret data.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical techniques to analyze data and develop models that can be used to make decisions. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for quantitative analysts. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for quantitative analysts who want to be able to accurately analyze data and develop models.
Mathematician
Mathematician
Mathematicians use mathematical and statistical techniques to solve problems. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for mathematicians. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for mathematicians who want to be able to accurately solve problems.
Computer Scientist
Computer Scientist
Computer scientists use mathematical and statistical techniques to design and develop computer systems. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for computer scientists. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for computer scientists who want to be able to accurately design and develop computer systems.
Software Engineer
Software Engineer
Software engineers use mathematical and statistical techniques to design and develop software. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for software engineers. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for software engineers who want to be able to accurately design and develop software.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical and statistical techniques to solve problems related to the design and operation of systems. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for operations research analysts. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for operations research analysts who want to be able to accurately solve problems related to the design and operation of systems.
Financial Risk Manager
Financial Risk Manager
Financial risk managers use mathematical and statistical techniques to assess and manage financial risk. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for financial risk managers. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for financial risk managers who want to be able to accurately assess and manage financial risk.
Data Scientist
Data Scientist
Data scientists use mathematical and statistical techniques to collect, analyze, and interpret data. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for data scientists. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for data scientists who want to be able to accurately collect, analyze, and interpret data.
Investment Analyst
Investment Analyst
Investment analysts use mathematical and statistical techniques to analyze data and develop models that can be used to make investment decisions. Probability for Actuaries: Introduction to Discrete Distribution provides a solid foundation in probability theory, which is essential for investment analysts. The course covers topics such as probability distributions, Bayes' theorem, and the law of total probability. This knowledge is essential for investment analysts who want to be able to accurately analyze data and develop models.
For more career information including salaries, visit:
OpenCourser.com/course/hd0i0q/probability
Reading list
We've selected 23 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
Probability for Actuaries: Introduction to Discrete Distribution.
This textbook provides a solid foundation in probability and statistics. It covers a wide range of topics, including probability distributions, statistical inference, and regression analysis. The book also includes numerous examples and exercises to help students understand the material.
Provides a comprehensive introduction to loss models. It valuable resource for students and professionals who need to learn the basics of these models.
Save
Provides an introduction to probability and statistics for risk management. It valuable resource for students and professionals who need to learn the basics of these subjects in the context of risk management.
Provides a comprehensive introduction to statistical learning. It valuable resource for students and professionals who need to learn the basics of these methods.
This textbook provides a comprehensive overview of mathematical statistics. It covers a wide range of topics, including probability theory, statistical inference, and regression analysis. The book also includes numerous examples and exercises to help students understand the material.
This textbook provides a comprehensive overview of probability and statistical inference. It covers a wide range of topics, including probability theory, statistical inference, and regression analysis. The book also includes numerous examples and exercises to help students understand the material.
Provides a thorough introduction to discrete mathematics, which branch of mathematics that deals with objects that can be counted or separated into distinct units. It valuable resource for students who need to learn the basics of discrete mathematics.
Provides an introduction to statistical methods for finance. It valuable resource for students and professionals who need to learn the basics of these methods.
Save
Provides a comprehensive introduction to deep learning. It valuable resource for students and professionals who need to learn the basics of these methods.
Provides a comprehensive introduction to reinforcement learning. It valuable resource for students and professionals who need to learn the basics of these methods.
Save
Provides a comprehensive introduction to causal inference. It valuable resource for students and professionals who need to learn the basics of these methods.
Provides a comprehensive introduction to Bayesian statistics. It valuable resource for students and professionals who need to learn the basics of these methods.
This textbook provides a clear and concise introduction to probability theory. It covers a wide range of topics, including probability distributions, statistical inference, and regression analysis. The book also includes numerous examples and exercises to help students understand the material.
This textbook provides a rigorous introduction to probability theory and measure theory. It covers a wide range of topics, including probability spaces, random variables, and stochastic processes. The book also includes numerous examples and exercises to help students understand the material.
This textbook provides a comprehensive overview of real analysis and probability theory. It covers a wide range of topics, including measure theory, integration, and stochastic processes. The book also includes numerous examples and exercises to help students understand the material.
This textbook provides a comprehensive overview of measure theory and probability theory. It covers a wide range of topics, including measure spaces, random variables, and stochastic processes. The book also includes numerous examples and exercises to help students understand the material.
This textbook provides a comprehensive overview of stochastic processes. It covers a wide range of topics, including Markov chains, Brownian motion, and diffusion processes. The book also includes numerous examples and exercises to help students understand the material.
Save
This textbook provides a comprehensive overview of probability theory. It covers a wide range of topics, including probability spaces, random variables, and stochastic processes. The book also includes numerous examples and exercises to help students understand the material.
This textbook provides a comprehensive overview of stochastic processes. It covers a wide range of topics, including Markov chains, Brownian motion, and diffusion processes. The book also includes numerous examples and exercises to help students understand the material.
Provides a comprehensive overview of numerical methods and algorithms. It covers a wide range of topics, including probability distributions, random number generation, and statistical inference. The book also includes numerous examples and exercises to help students understand the material.
This textbook provides a comprehensive overview of simulation and the Monte Carlo method. It covers a wide range of topics, including random number generation, sampling methods, and variance reduction techniques. The book also includes numerous examples and exercises to help students understand the material.
Provides a comprehensive overview of data analysis using Python. It covers a wide range of topics, including data cleaning, data manipulation, and data visualization. The book also includes numerous examples and exercises to help students understand the material.
Save
Provides a comprehensive overview of data science using R. It covers a wide range of topics, including data cleaning, data manipulation, and data visualization. The book also includes numerous examples and exercises to help students understand the material.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hd0i0q/probability
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Effort
4 to 6 hours per week for 9 weeks
Level
Intermediate
Via
edX
Institution
The University of Wisconsin-Madison
Instructor
Gordon Enderle
Session
May 15, 2024
- May 14, 2025
Language
English
Transcript
English
Good to know
Covers math and statistical theory that's commonly found in actuarial work
Offers a mix of text-based lessons, videos, and interactive exercises
Provides a strong grasp of the foundational concepts of probability theory
Taught by seasoned instructors who have made significant contributions to the field of actuarial science
Employs a variety of real-world examples to illustrate the practical applications of probability theory in actuarial work
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