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Information
| Code | ST0034 |
| Name | Bayesyen Inference |
| Term | 2024-2025 Academic Year |
| Term | Spring |
| Duration (T+A) | 4-2 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 5 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
The aim of this course is to show Bayesian Inference to Ecologists
Course Content
Introduction to Probability, Introduction to Bayesian Inference, Difference between classical and Bayesian inference, Principle of Bayesian inference , Prior distributions, MCMC Simulation, Bayesian estimation of descriptive statistics, Bayesian application of some statistical tests (t-test, ANOVA, Regression, Bayesian fisheries stock assessment analysis, Bayesian logistic regression OpenBUGS/JAGS/WinBUGS/R2Bugs applications
Course Precondition
None
Resources
GÜNDOĞDU, Sedat, and Makbule Baylan. "COMPARISON OF BAYESIAN ESTIMATION AND CLASSICAL ESTIMATION OF BRUSHTOOTH LIZARDFISH (Saurida lessepsianus RUSSELL, GOLANI & TIKOCHINSKI 2015) GROWTH." Scientific Papers: Series D, Animal Science-The International Session of Scientific Communications of the Faculty of Animal Science 59 (2016). Link, William A., and Richard J. Barker. Bayesian inference: with ecological applications. Academic Press, 2009. Carlin, Bradley P., and Thomas A. Louis. Bayesian methods for data analysis. CRC Press, 2008. McCarthy, Michael A. Bayesian methods for ecology. Cambridge University Press, 2007. Lunn, David J., et al. "WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility." Statistics and computing 10.4 (2000): 325-337. Ntzoufras, Ioannis. Bayesian modeling using WinBUGS. Vol. 698. John Wiley & Sons, 2011. Kéry, Marc, and Michael Schaub. Bayesian population analysis using WinBUGS: a hierarchical perspective. Academic Press, 2011. Gelman A., Carlin J.B., Sten H.S. and Rubin D.B. (2003). Bayesian Data Analysis. 2nd edition. London: Chapman and Hall. Congdon P. (2010). Applied Bayesian Hierarchical Methods. Chapman and Hall/CRC.
Notes
Akar, M., & Gündogdu, S. (2014). BAYES TEORISININ SU ÜRÜNLERINDE KULLANIM OLANAKLARI/The usage of bayes theory in fisheries sciences. Journal of FisheriesSciences.Com, 8(1), 8-16. Retrieved from https://search.proquest.com/docview/1493992812?accountid=15725
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | With this course, students learn the basic principle of Bayesian Inference. |
| LO02 | Comprehends the differences between the Bayesian approach and the classical approach |
| LO03 | Learns Bayesian version of Simple and Multiple Linear Regression |
| LO04 | Gains the skill of performing Bayesian hypothesis testing and one-way ANOVA analysis with Bayesian approach |
| LO05 | Learn the Bayesian way of Nonlinear Regression and Growth Models |
| LO06 | Learns the Bayesian analysis of the stock/stock assessment relationship fish populations based on mark-recapture. |
| LO07 | Gains the ability to perform Markov Chain Monte Carlo simulation with the help of OpenBUGS/JAGS/winBUGS/R2bugs |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Develops theoretical and practical knowledge in the field of Marine Biology, Inland Water Biology or Basic Sciences in Fisheries at the level of expertise. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Comprehends interactions between Fisheries Basic Sciences and other disciplines. | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Determines the strategies related to the field of specialization in Basic Sciences of Aquaculture; explains the methods and techniques, measurement and concepts. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Produces new information and theories by interpreting and synthesising the information from other disciplines and uses the theoretical and practical information from their field of study in Fisheries Basic Science. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Collects data, interprets results and suggests solutions by using dialectic research methodology in the certain field of Marine and Inland Water Biology and Fisheries Basic Sciences. | 3 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Independently plans, designs and performs a certain project in the field of Fisheries Basic Sciences. | 3 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Produces solutions by improving new strategic approaches and taking responsibilities for the potential problems in the field of study as an individual or team member. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Determines the requirements for Fishery Basic Science education, reaches the resources, critically interpretes knowledge and skills and gains experience to direct the education. | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Has positive stance on the lifelong education and uses it for the public benefit by using the gained theoretical and practical knowledge in the field of Marine and Inland Water Biology and Fisheries Basic Sciences. | |
| PLO10 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Follows the current topics and improvements in the field of Fisheries Basic Sciences, publishes and presents the research results, contributes to constitution of a public conscience in the field of interest. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Effectively communicates about the field of Marine and Inland Water Biology and Fisheries Basic Sciences by using written and oral presentation tools, follows up and criticizes the meetings and seminars. | |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Follows up international publications and communicates with international collaborators by using language skills. | 3 |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Uses the communication and information technologies about the field of interest in an advanced level. | 5 |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Conforms, controls and teaches social, cultural and scientific ethics in the investigation and publication process of the data related with the field of interest. | 5 |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Improves strategies, politics and application codes by following scientific and technological developments on the certain field of Marine and Inland Water Biology and Fisheries Basic Sciences. Investigates and extends the results on behalf of public in frame of total quality management process. | 5 |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | Uses the abilities and experiences on applications and solving problems that gained during the MSc education for the interdisciplinary studies. | 5 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Probability | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 2 | Bayes theory: The Introduction | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 3 | Bayes theory | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım |
| 4 | Difference between classical and Bayesian inference | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım |
| 5 | Principle of Bayesian inference | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım |
| 6 | What is prior distributions | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 7 | The types of Prior distributions | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 8 | Mid-Term Exam | Notes from previous lessons should be read. | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
| 9 | MCMC Simulation | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 10 | Bayesian estimation of descriptive statistics and its BUGS applications | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 11 | Bayesian application of some statistical tests (t-test, ANOVA) and its BUGS applications | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 12 | Bayesian application of some statistical tests (Regression) and its BUGS applications | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 13 | Bayesian fisheries assessment analysis and its BUGS applications | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 14 | Bayesian fisheries stock assessment analysis and its BUGS applications | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 15 | Bayesian logistic regression and its BUGS applications | The relevant topic is read from the lecture notes. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 16 | Term Exams | Notes from previous lessons should be read. | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
| 17 | Exams | Notes from previous lessons should be read. | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||