## "Formal Informal: Imprecise Probabilities" | MCMP joint event with Foundations of Statistics (4 Feb, 2013)

- Details
- Last Updated on Monday, 04 February 2013 16:42

On Monday, 4 February, 2013, the MCMP is meeting the LMU Stats Department once again: The third edition of our series "Formal Informal" will center about "Imprecise Probabilities" this time. Join us for the discussion in an open round at 6:00pm, Alte Bibliothek, room 245 (Ludwigstraße 33). Presenters will be Thomas Augustin (Statistics/LMU), Radin Dardashti (MCMP/LMU), Marco Cattaneo (Statistics/LMU), Seamus Bradley (MCMP/LMU), and Stephan Hartmann (MCMP/LMU).

From the manifesto: Probability plays a fundamental role in our attempt to grasp and quantify uncertainty. However, considering the common case of limited knowledge about an event, the assignment of precise probabilities can be regarded as a limitation of the approach. Generalising probability theory to imprecise or interval probabilities offers a broader framework within which to discuss uncertainty. This approach has recently become quite popular due to its successful applications in many areas, ranging from social epistemology to econometrics and artificial intelligence.

This edition of Formal Informal will discuss foundations, applications, and problems of Imprecise Probabilities within statistics, philosophy, and physics.

Download the invitation here as a PDF document.

## Imprecise prediction and reliability in intervals - talk at WPMSIIP'12 (13 September, 2012)

- Details
- Last Updated on Friday, 21 September 2012 15:09

On 13 September, 2012, Roland Poellinger gives a talk at the Fifth Workshop on Principles and Methods of Statistical Inference with Interval Probability (WPMSIIP'12) in Munich, the title: "Superimposing Imprecise Evidence onto Stable Causal Knowledge: Analyzing ‘Prediction’ in the Newcomb Case". The talk sketches a possible extension of the CKP framework and outlines an agenda for introducing interval probabilities into causal reasoning, where specific concepts need to be formalized in an imprecise way – (un)reliability as quality of prediction in the Newcomb case. From the abstract:

In this talk I will prepare the ground for a understanding of causality that enables the causal decision theorist to answer Nozick’s challenge with the solution of one-boxing by drawing on the framework of *causal knowledge patterns*, i.e., Bayes net causal models built upon stable causal relations (cf. Pearl 1995 and 2000/2009) augmented by non-causal knowledge (*epistemic contours*). This rendition allows the careful re-examination of all relevant notions in the original story and facilitates approaching the following questions:

- How may causality in general be understood to allow causal inference from hybrid patterns encoding subjective knowledge?
- How can the notion of
*prediction*be analyzed – philosophically and formally? - If all relations given in the model represent stable causal knowledge, how can imprecise evidence be embedded formally? Or in other words: How can the
*unreliable predictor*be modeled without discarding the core structure?

Finally, in what way could *unreliable prediction* be modeled with interval probability, as motivated by considerations in Nozick’s treatise? And what should be the interpretation of such a rendition?

## Welcome to logic.rforge!

- Details
- Last Updated on Saturday, 02 February 2013 09:19

This is the personal academic homepage of Dr. Roland Poellinger, research fellow at the Munich Center for Mathematical Philosophy (MCMP/LMU). His main research interests center around algorithmic methods in logic, causal modeling, and probabilistic reasoning in Bayesian networks. He is exploring ways of communicating science across audiences to shape new channels for mathematical philosophy. In 2013 he is teaching philosophy of science at Johannes Gutenberg-Universität Mainz, formal theories of causation at the University of Pécs in Hungary, and topics from philosophy and robotics at Venice International University. [More in About]

## "Formal Informal: Inductive Logic and Probabilities" | MCMP joint event with Foundations of Statistics (12 July, 2012)

- Details
- Last Updated on Friday, 21 September 2012 15:09

On Thursday, 12 July, 2012, the MCMP is meeting the LMU Stats Department once again: The second edition of our series "Formal Informal" will center about "Inductive Logic and Probabilities" this time. Join us for the discussion in an open round at 6:30pm, Alte Bibliothek, room 245 (Ludwigstraße 33). Presenters will be Karine Fradet (Philosophy, Université de Montrèal), Frederik Herzberg (Math. Economics, Bielefeld/MCMP), and Christina Schneider (Philosophy & Statistics, LMU).

From the manifesto: Carnap, who occupies a central place in the development of inductive logic, showed that the disagreements between the interpretation of probabilities as a state of the world and as a state of knowledge of the observer were vain since they were not about the same concept. He concentrated on the second of these two concepts, inductive probabilities, drew the foundations of inductive logic, systematized aspects and approaches, and presented the different methods not as competing against each other, but as part of a system, each perspective being a point on the continuum of the inductive methods.

This edition of Formal Informal will collect, sort, and discuss foundations, applications, and problems of inductive methodology, bridging views from philosophy and statistics.

Download the invitation here as a PDF document.

## "Formal Informal: The Markov Assumption" | MCMP joint event with Foundations of Statistics (13 June, 2012)

- Details
- Last Updated on Friday, 21 September 2012 15:09

On 13 June, 2012, the MCMP is meeting the LMU Stats Department again: "Formal Informal" will center about "The Markov Assumption". Join us for the discussion in an open round at 6:30pm, Alte Bibliothek, room 245 (Ludwigstraße 33). Presenters will be Conor Mayo-Wilson (CMU Philosophy, currently visitor at the MCMP), Marco Cattaneo (LMU Statistics), and Roland Poellinger (MCMP/LMU).

From the manifesto: Bayesian nets are a powerful means of representing conditional independencies between variables in compact manner. Whatever the size of the domain, consistent inference is facilitated by one simple local requirement: The Markov assumption states that a variable is independent of all other non-successors given the values of its parents in the graph. In causal guise: Direct causes screen off their direct effects from other causal influences. What other ways of reading the Markov assumption are there? Why is it justified? Where does it hold? How can it be bent?

Download the invitation here as a PDF document.

### More Articles...

- Talk "Newcomb's Paradox – Wissen ordnen und erschließen in hybriden Netzen" @ Foundations of Statistics (16 May, 2012)
- Talk "What is Causal Knowledge?" @ LMU Centrum für Informations- und Sprachverarbeitung (19 Jan, 2012)
- Research-based teaching with workshop videos
- Teddy Seidenfeld at the MCMP & Statistics Department joint event, 29 July 2011