Course Objectives
Acquire basic knowledge about Bayesian statistics and stochastic processes.
1 To understand the basic idea of Bayesian statistics.
2 To understand the basic idea of stochastic processes.
Rubric
| Excellent | Good | Acceptable | Unacceptable |
| Achievement 1 | A good understanding of the basic idea of Bayesian statistics. | Understand about 70% of the basic idea of Bayesian statistics. | Understand about 60% of the basic ideas of Bayesian statistics. | Not understand about 60% of the basic ideas of Bayesian statistics. |
| Achievement 2 | A good understanding of the basic idea of stochastic processes. | Understand about 70% of the basic idea of stochastic prolesses. | Understand about 60% of the basic ideas of stochastic processes. | Not understand about 60% of the basic ideas of stochastic processes. |
Assigned Department Objectives
Teaching Method
Outline:
General or Specialized : Specialized
Field of learning : Mathematics / Physics (Specialized Subjects)
Required, Elective, etc. : Elective must complete subjects
Foundational academic disciplines : Mathematical science / Mathematics / Basic analysis
Relationship with Educational Objectives : This class is equivalent to "(3) Acquire foundation knowledge of the major subject area".
Relationship with Educational Objectives : The main goals of learning / education in this class are "(A) , A-1. "
Course outline : Explain the basic theory of Bayesian statistics and stochastic processes.
Style:
Course method : In addition to lectures, practice in group discussions to learn the basics of algebra.
Grade evaluation method: Two regular examinations (50%) and the exercise reports (50%). In addition, depending on the grade, an additional report may be imposed.
Notice:
Characteristics of Class / Division in Learning
Course Plan
|
|
|
Theme |
Goals |
| 1st Semester |
| 1st Quarter |
| 1st |
What is a hypothesis test? |
Understand the difference between Bayesian statistics and frequency theory tests
|
| 2nd |
Basic hypothesis testing of Bayesian statistics |
Learn the basics of basic hypothesis testing
|
| 3rd |
Hypothesis test based on Bayesian statistics frequency theory |
Learn the posterior odds ratio and the hypothesis test using it
|
| 4th |
Bayes factor |
Understand Bayes factor and learn hypothesis testing using it
|
| 5th |
Bayesian statistics hypothesis test in pointless hypothesis |
Learn the test when the null hypothesis is a point
|
| 6th |
Problems and summary in the hypothesis test of Bayesian statistics |
Understand the problems of Bayesian statistics
|
| 7th |
First term midterm exam |
|
| 8th |
Binomial process |
Learn the probability calculation of the binomial process
|
| 2nd Quarter |
| 9th |
Poisson process |
Learn the probability calculation of Poisson process
|
| 10th |
Markov chain |
Understanding Markov Chains and State Probabilities
|
| 11th |
Markov chain |
Understanding Markov Chains and State Probabilities
|
| 12th |
Brownian motion |
Understanding Brownian motion as a stochastic process
|
| 13th |
Stochastic differential equation |
Learn how to solve basic stochastic differential equations
|
| 14th |
Chaos and stochastic differential equations |
Understand stochastic differential equations for chaos
|
| 15th |
Last term exam |
Answers and explanations for the final exam
|
| 16th |
|
|
Evaluation Method and Weight (%)
| Examination | Presentation | Mutual Evaluations between students | Behavior | Portfolio | Other | Total |
| Subtotal | 50 | 0 | 0 | 0 | 0 | 50 | 100 |
| Basic Proficiency | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Specialized Proficiency | 50 | 0 | 0 | 0 | 0 | 50 | 100 |
| Cross Area Proficiency | 0 | 0 | 0 | 0 | 0 | 0 | 0 |