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Bayesian Models in Psychology

Informacje ogólne

Kod przedmiotu: 2500-EN-F-204 Kod Erasmus / ISCED: 14.4 / (0313) Psychologia
Nazwa przedmiotu: Bayesian Models in Psychology
Jednostka: Wydział Psychologii
Grupy: Academic basket
Elective courses
electives for 4 and 5 year
Methodology, Statistics and Psychometrics basket
Punkty ECTS i inne: 4.00
Język prowadzenia: angielski
Rodzaj przedmiotu:

fakultatywne

Skrócony opis: (tylko po angielsku)

The main goal of this course is to familiarize students with basics of

Bayesian data analysis and its applications in psychology.

Pełny opis: (tylko po angielsku)

Bayesian data analysis is an alternative to the classical (frequentist)

approach to statistics, that deals directly with problems of uncertainty

and probability in research problems. As pointed by many authors, power,

flexibility, and easiness of interpretation of Bayesian data analysis makes

it a natural candidate to approach problems in modeling of psychological

processes.

During this lab we will learn the basics of Bayesian approach to statistics.

We will learn strengths of Bayesian alternatives to t-test, ANOVA,

correlation and regression analyses, and how to perform them using

open-source software (R and JAGS).

Literatura: (tylko po angielsku)

1. Bayes Rule and it’s applications

- Krushke, chapter 2 and 5

2. Binomial and Normal Models

- Krushke, chapter 4 and 6

3. More on priors

- additional chapter from Gill (2014), scan provided by lecturer

4. Intro to Bayesian computation with R

- Krushke, chapter 3

5. Computing Bayesian models with simulations (in R)

6. Basics of Markov Chain Monte Carlo

- Krushke, chapter 7

7. Using JAGS and rjags

- Krushke, chapter 8

8. Midterm exam

9. Bayesian t-test

- Krushke, chapter 18

10. Comparing more than two groups

- Krushke, chapter 19

11. Factorial design analysis in Bayesian approach

- Krushke, chapter 20

12. Bayesian regression

- Krushke, chapter 17

13. Bayesian multiple regression

- Krushke, chapter 18

14. Bayesian logistic regression

- Krushke, chapter 21

15. Final exam

Handbook for this course is:

Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS,

and Stan. Academic Press.

Efekty uczenia się: (tylko po angielsku)

Upon completion of this course:

 students know basics of Bayesian data analysis and it’s theoretical

underpinnings

 students know how to perform basic statistical computations with R

 students know how to perform basic Bayesian analyses with R and

JAGS

 students know potential applications of Bayesian models in social and

behavioral sciences and are able use some of them in their own

research

Metody i kryteria oceniania: (tylko po angielsku)

Students are allowed to miss 2 classes without excuse, 2 more classes in

case of excuse, but will not pass the course in case of more than 4

absences.

Additional work is assigned in case more than 2 classes are missed (even

in cases of valid excuse).

The Final grade will be determined by three components: midterm and

final exam scores and amount of points gathered from the home

assignments throughout the semester.

The final grade will be the weighted average computed according to the

following formula: 30% * (midterm score) + 30% * (home assignments) +

40% * (final exam score) = total score

Total score and both exam scores should be at least at the 50% level to

pass the course.

Grading scale:

95%+ = 5!

90-94% = 5

80-89% = 4.5

70-79% = 4

60-69% = 3.5

50-59% 59% = 3

below 50% = 2 (fail)

Zajęcia w cyklu "Semestr zimowy 2019/20" (zakończony)

Okres: 2019-10-01 - 2020-01-27
Wybrany podział planu:


powiększ
zobacz plan zajęć
Typ zajęć: Ćwiczenia, 30 godzin, 13 miejsc więcej informacji
Koordynatorzy: Wiktor Soral
Prowadzący grup: Wiktor Soral
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Zaliczenie na ocenę
Ćwiczenia - Zaliczenie na ocenę
Uwagi: (tylko po angielsku)

Basket 7. Methodology and statistics

Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Warszawski.