Bayesian Statistics for Masters [Bayesian Stats]

Dozent/in:

Alexander Pastukhov

Angaben:

Seminar, 2 SWS, ECTS: 3

Termine:

Mi, 12:00 - 14:00, M3N/-1.19

Voraussetzungen / Organisatorisches:

Some Bachelor level knowledge R is beneficial, but no prior knowledge beyond high school algebra is required. For Ba / Ma Psychology only!

Inhalt:

The purpose of this seminar, is to build your intuition and understanding of (Bayesian) statistics from ground up, starting with a concept of probability as counting, continuing to simple linear models and advancing to more complicated multilevel linear models. The linear models that we study underpin all classic statistical test: t-test, ANOVA, rm ANOVA, ANCOVA, MANOVA, Pearson correlation, etc. You will learn about their simple common structure, understand how to design such models by hand (much simpler than you think), and, most importantly how to interpret and evaluate these models (much harder than you think) using causal calculus tools and information criteria.
Learning Goals: In this seminar, you will learn how to build a statistical model from the ground up with the goal of being able to build a customized model for any statistical problem and analysis. After this course you will understand that a linear regression, a T-test, an ANOVA, or an ANOCOVA all refer to the same simple linear model that you can build yourself. The aim is to make sure that you will know exactly what your analysis does and why you are doing it in this way.
Course Method: This seminar assumes no prior knowledge on your part. We will start with a basic concept of probability-as-counting and proceed to understanding what statistical models are and how to build them. Over the course of the seminar, we will gradually move forward to more advanced topics learning how to identify spurious associations, infer causality, evaluate models, or perform power analysis. Forming a book club we will read "Statistical Rethinking" by Richard McElrath. It is an excellent introductory statistics book that explain even most intimidating topics very clearly, links all seemingly discrepant topics together, and has plenty of examples in R. We will read one chapter every week, practice build models, and discuss the topics and questions during the seminar.

Empfohlene Literatur:

"Statistical Rethinking: A Bayesian Course with Examples in R and Stan" by Richard McElreath https://www.oreilly.com/library/view/statistical-rethinking/9781482253481/

Decision making: emotions, formal logic, and games

Dozent/in:

Alexander Pastukhov

Angaben:

Seminar

Termine:

Do, 10:00 - 12:00, MG2/02.04

Inhalt:

How do we make our decisions? How do animals make theirs? What can we learn from them and what is unique to us?
The course covers broad range of topics that introduce the emotional and analytical decision making systems and their interaction in your daily experience. We start with the low level perceptual decision making (signal detection theory, sequential analysis framework, race models) that despite their simplicity are ubiquitous and appear to underpin decision making at all levels starting from simple perceptual decision (is this car moving in my direction?) to high level ones (should I take that job?). We then continue to gambling to understand how we make decision under high uncertainty. Next, we will cover the topic of emotional decision making and it influences us (e.g., how marketing sways your purchasing decisions). Then comes the game theory to understand how do you make the decision, if you know that its outcome depends on decision of others, who are trying to second guess you (in the process, you will learn why ducks are optional decision makers). We will look into the free will and see whether and how can you be sure that you made the decision and moral judgements in humans and animals.
The course is taught in English, which will allow you build vocabulary on the topic.

Game theory, emotions, and formal logic in decision making, masters

Dozent/in:

Alexander Pastukhov

Angaben:

Seminar

Termine:

Mi, 10:00 - 12:00, MG2/00.09

Inhalt:

How do we make our decisions? How do animals make theirs? What can we learn from them and what is unique to us?
The course covers broad range of topics that introduce the emotional and analytical decision making systems and their interaction in your daily experience. We start with the low level perceptual decision making (signal detection theory, sequential analysis framework, race models) that despite their simplicity are ubiquitous and appear to underpin decision making at all levels starting from simple perceptual decision (is this car moving in my direction?) to high level ones (should I take that job?). We then continue to gambling to understand how we make decision under high uncertainty. Next, we will cover the topic of emotional decision making and it influences us (e.g., how marketing sways your purchasing decisions). Then comes the game theory to understand how do you make the decision, if you know that its outcome depends on decision of others, who are trying to second guess you (in the process, you will learn why ducks are optional decision makers). We will look into the free will and see whether and how can you be sure that you made the decision and moral judgements in humans and animals.
The course is taught in English, which will allow you build vocabulary on the topic.

Introduction to Bayesian Statistics [Statistical Rethinking]

Dozent/in:

Alexander Pastukhov

Angaben:

Seminar, 2 SWS, ECTS: 3

Termine:

Do, 12:00 - 14:00, MG2/01.09

Voraussetzungen / Organisatorisches:

Some Bachelor level knowledge R is beneficial, but no prior knowledge beyond high school algebra is required. For Ba / Ma Psychology only!

Inhalt:

The purpose of this seminar, is to build your intuition and understanding of (Bayesian) statistics from ground up, starting with a concept of probability as counting, continuing to simple linear models and advancing to more complicated multilevel linear models. The linear models that we study underpin all classic statistical test: t-test, ANOVA, rm ANOVA, ANCOVA, MANOVA, Pearson correlation, etc. You will learn about their simple common structure, understand how to design such models by hand (much simpler than you think), and, most importantly how to interpret and evaluate these models (much harder than you think) using causal calculus tools and information criteria.

Learning Goals: In this seminar, you will learn how to build a statistical model from the ground up with the goal of being able to build a customized model for any statistical problem and analysis. After this course you will understand that a linear regression, a T-test, an ANOVA, or an ANOCOVA all refer to the same simple linear model that you can build yourself. The aim is to make sure that you will know exactly what your analysis does and why you are doing it in this way.

Course Method: This seminar assumes no prior knowledge on your part. We will start with a basic concept of probability-as-counting and proceed to understanding what statistical models are and how to build them. Over the course of the seminar, we will gradually move forward to more advanced topics learning how to identify spurious associations, infer causality, evaluate models, or perform power analysis. Forming a book club we will read "Statistical Rethinking" by Richard McElrath. It is an excellent introductory statistics book that explain even most intimidating topics very clearly, links all seemingly discrepant topics together, and has plenty of examples in R. We will read one chapter every week, practice build models, and discuss the topics and questions during the seminar.

Empfohlene Literatur:

"Statistical Rethinking: A Bayesian Course with Examples in R and Stan" by Richard McElreath https://www.oreilly.com/library/view/statistical-rethinking/9781482253481/

Schlagwörter:

statistics, bayesian statistics

Neuroscience of Consciousness

Dozent/in:

Alexander Pastukhov

Angaben:

Seminar, 2 SWS, ECTS: 3

Termine:

Do, 8:00 - 10:00, M3N/03.29

Inhalt:

What is consciousness and how can we study it? The purpose for this seminar is to show why answering the first question is hard (actually, impossible at the moment) and how nonetheless we can build understanding of consciousness by examining the brain. We will start by looking at questions that philosophers raised (What is like to be a bat? Are you a philosophical zombie? Who is inside the Chinese room? What is a thought about a thought called?). Next we will look for missing consciousness in clinical cases (When are you actually dead and how can we know it? Can you see without primary visual cortex? When your arm is not your arm anymore? How many personalities can you squeeze into two hemispheres?), unconscious processing (Why cannot you see the flicker, if your primary visual cortex can? Can you pay attention to an invisible target?), free will (How do you know you did it? Why do you feel responsible, if you favorite team wins or loses?), false memory (Are you sure this happened to you at all?), and consciousness in animals (Again, what is it like to be a bat? Can animals read others minds?)
The course is taught in English, which will allow you build vocabulary on the topic.

Empfohlene Literatur:

"Consciousness : an introduction" by Susan Blackmore https://katalog.ub.uni-bamberg.de/query/BV042668728

Scientific Studies of Consciousness, Masters level

Dozent/in:

Alexander Pastukhov

Angaben:

Vorlesung, 2 SWS, ECTS: 3

Termine:

Mi, 8:00 - 10:00, M3N/-1.19

Inhalt:

What is consciousness and how can we study it? The purpose for this seminar is to show why answering the first question is hard (actually, impossible at the moment) and how nonetheless we can build understanding of consciousness by examining the brain. We will start by looking at questions that philosophers raised (What is like to be a bat? Are you a philosophical zombie? Who is inside the Chinese room? What is a thought about a thought called?). Next we will look for missing consciousness in clinical cases (When are you actually dead and how can we know it? Can you see without primary visual cortex? When your arm is not your arm anymore? How many personalities can you squeeze into two hemispheres?), unconscious processing (Why cannot you see the flicker, if your primary visual cortex can? Can you pay attention to an invisible target?), free will (How do you know you did it? Why do you feel responsible, if you favorite team wins or loses?), false memory (Are you sure this happened to you at all?), and consciousness in animals (Again, what is it like to be a bat? Can animals read others minds?)
The course is taught in English, which will allow you build vocabulary on the topic.

Empfohlene Literatur:

"Consciousness : an introduction" by Susan Blackmore https://katalog.ub.uni-bamberg.de/query/BV042668728