- GR1 (Yao Chen & Yuping Liu) A Discussion on LAZY propagation
- GR2 (Daniela Gawehns, Amira Khalil & Niek Mereu) The incomparable: probabilistic graphs for fMRI data analysis
- GR3 (Francesco Bortolussi, Martha Kartalidou, Jeremiah Okai & Andres Felipe Ramos Padilla) A survey on Cognitive sciences through Bayesian modeling
- GR4 (Xin Guo & Yuanmin Xu) Applying Bayesian networks to Semantic Image Understanding
- GR5 (Nicole Ostlund, Ina Quataert, Meeke Rijnen & Marieke Vinkenoog) Bayesian Networks for fMRI Analysis
- Applying Bayesian networks to diagnosis and image recognition
- Bayesian reasoning in court
- Variable elimination
- Influence diagrams, their evaluation and their applications
- Solving influence diagrams using belief networks and transformations
- Each group will discuss the papers within one chosen topic in meetings (may be outside the course hours) scheduled with the lecturer.
- The topic must be elaborated in a
*survey paper*, where you summarise the topic, and express you own views about it (max. 10 pages, 10pt font size, A4). The paper can be written__individually__or__in groups__. In the latter case, each student should made clear his/her contribution to the paper.

- The survey paper should be submitted
**before 21nd June 2017, 24:00**, via email to Peter Lucas (plucas AT liacs.nl).**Note: you will get a mark for the survey paper!** -
**Philosophical foundations of reasoning with uncertainty**- Book: BAI 1.1-1.5
- Uncertainty in AI systems: an overview (Chapter 1; Pearl, 1988) [pdf]
- Why isn't everyone a Bayesian? (Efron, 1986) [pdf]
- Judgement under uncertainty: heuristics and biases (Tversky and Kahneman, 1974) [pdf]
- Languages and designs for probabilistic judgement (Shafer and Tversky, 1985) [pdf]
- Subjective probability (Kahneman and Tversky, 1972) [pdf]
- Fallacies in legal reasoning (Fenton et al., 2008) [pdf]

**Structure learning of BNs**- Book BAI 6.3, chapter 8;
- First paper on structure learning (Cooper and Herskovits, 1996) [pdf]
- Learning as search (Castelo and Kocka, 2003) [pdf]
- Use of genetic algorithms in BN structure learning (Larranaga et al., 1996) [pdf]
- Constraint-based learning (Cheng et al., 2008) [pdf]
- Constraint-based learning follow-up (Chichering and Meek, 2006) [pdf]
- The max-min hill-climbing Bayesian network structure learning algorithm

(Tsamardinos, Brown and Aliferis, 2006) [pdf]

**Decision networks and influence diagrams**

*Decision networks, a.k.a. influence diagrams, have been developed by extending Bayesian networks to allow a compact representation and reasoning under uncertainty in the structuring and analysis of complex decision situations. This is done by providing a graphical representation of the interrelationships among information (random variables), preferences (utility variables) and actions (decision variables) of the decision maker, and probabilistic assessment of the expected utility given the quantified decision basis.*- Book: BAI 4.1-4.4, 9.3.7
- An anytime algorithm for evaluating unconstrained influence diagrams (Luque, Nielsen and Jensen) [pdf]
- Influence diagrams (Howard and Matheson, 2005) [pdf]
- Evaluating influence diagrams (Shachter, 1987) [pdf]
- A method for using Bayesian (belief) networks as influence diagrams (Cooper, 1988) [pdf]

**Probabilistic inference**

*During the lectures, a broad overview of inference methods has been discussed, somewhat focused on Pearl's algorithm and other exact belief propagations methods. Of course, in the last couple of decades much more work done in this area. Some more specialised topics can be studied in the seminar.*

- On probabilistic inference by weighted model counting (Chavira and Darwiche, 2008) [pdf]
- LAZY propagation: a junction tree inference algorithm based on lazy evaluation (Madsen and Jensen, 1999) [pdf]
- A simple approach to Bayesian network computations (Zhang and Poole, 1994) [pdf]
- Exploiting causal independence in Bayesian network inference (Zhang and Poole, 1996) [pdf]

**Probabilistic logics and relational learning**

*In recent years, several practical approaches have appeared that aim to integrate logic and probability theory. While Bayesian and Markov networks are in some sense propositional, logical languages allow you to reason about objects and classes of objects. These approaches have, for example, been applied to learn relationships between objects (relational learning).*- Markov logic networks (Richardson and Domingos, 2006) [pdf]
- ProbLog (Kimmig et al., 2010) [pdf]
- CP-Logic (Vennekens, Denecker and Bruynooghe, 2009) [pdf]
- The Independent Choice Logic for modelling multiple agents under uncertainty (Poole, 1997) [pdf]
- Object-oriented Bayesian networks (Koller and Pfeffer, 1997) [pdf]

**Qualitative probabilistic networks (QPNs)**

*Several formalisms have been developed that consider uncertainty relationships that are qualitative, i.e., these approaches do not make use of numerical values as in probability theory. These kind of formalisms can be used to solve problems where numerical values are neither necessary nor appropriate.*

- Book: BAI, page 250
- Fundamental concepts of qualitative probabilistic networks (Wellman, 1990) [pdf]
- Refining reasoning in qualitative probabilistic networks (Parsons, 1995) [pdf]
- Bayesian network modelling through qualitative patterns (Lucas, 2005) [pdf]
- Enhanced qualitative probabilistic networks for resolving trade-offs (Renooij and Van der Gaag, 2008) [pdf]

**Exploiting expert knowledge in Bayesian network learning**

*How can you integrate expert knowledge in learning.*- Paper by Zhou, Fenton
- Paper by Hossein et al

**Image interpretation, control and cognitive modelling**

*Various studies have demonstrated the powerful modelling and reasoning capabilities of PGMs by applying them to complex problems such as brain signal analysis, image interpretation, and perception of intentions and mental states in virtual agents.*

- Learning effective brain connectivity with dynamic Bayesian networks (Rajapakse and Zhou, 2007) [pdf]
- Bayesian models of object perception (Kersten and Yuille, 2003) [pdf]
- Modeling aspects of Theory of Mind with Markov random fields (Butterfield et al., 2009) [pdf]
- Diagnose the mild cognitive impairment by constructing Bayesian network with missing data

(Sun, Tang, et al., 2011) [pdf] - Evidence-driven image interpretation by combining implicit and explicit knowledge in a Bayesian network

(Nikolopoulos, Papadopoulos, et al., 2011) [pdf]