Introducing situational signs in qualitative probabilistic networks (original) (raw)

Context-specific sign-propagation in qualitative probabilistic networks

2002

Qualitative probabilistic networks are qualitative abstractions of probabilistic networks, summarising probabilistic influences by qualitative signs. As qualitative networks model influences at the level of variables, knowledge about probabilistic influences that hold only for specific values cannot be expressed. The results computed from a qualitative network, as a consequence, can be weaker than strictly necessary and may in fact be rather uninformative.

Inference in qualitative probabilistic networks revisited

International Journal of Approximate Reasoning, 2009

Qualitative probabilistic networks (QPNs) are basically qualitative derivations of Bayesian belief networks. Originally, QPNs were designed to improve the speed of the construction and calculation of these networks, at the cost of specificity of the result. The formalism can also be used to facilitate cognitive mapping by means of inference in sign-based causal diagrams. Whatever the type of application, any computer based use of QPNs requires an algorithm capable of propagating information throughout the networks. Such an algorithm was developed in the 1990s. This polynomial time sign-propagation algorithm is explicitly or implicitly used in most existing QPN studies.

Probabilities for a probabilistic network: a case study in oesophageal cancer

Artificial Intelligence in Medicine, 2002

With the help of two experts in gastrointestinal oncology from the Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, a decision-support system is being developed for patient-specific therapy selection for oesophageal carcinoma. The kernel of the system is a probabilistic network that describes the characteristics of oesophageal carcinoma and the pathophysiological processes of invasion and metastasis. While the construction of the graphical structure of the network was relatively straightforward, probability elicitation with existing methods proved to be a major obstacle. We designed a new method for eliciting probabilities from experts that combines the ideas of transcribing probabilities as fragments of text and of using a scale with both numerical and verbal anchors for marking assessments. The method allowed us to elicit the many probabilities required for our network in little time. Using data from 185 patients, we conducted an evaluation study to assess the quality of the probabilities obtained. We found that for 85% of the patients, our probabilistic network yielded the correct outcome.

Refining reasoning in qualitative probabilistic networks

1995

Abstract In recent years there has been a spate of papers describing systems for probabilisitic reasoning which do not use numerical probabilities. In some cases the simple set of values used by these systems make it impossible to predict how a probability will change or which hypothesis is most likely given certain evidence. This paper concentrates on such situations, and suggests a number of ways in which they may be resolved by re ning the representation.

Probabilities for a Probabilistic Network: A Case-Study in Oesophageal Carcinoma

Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support

2011

With the current trend toward pervasive health care, personalised health care, and the ever growing amount of evidence coming from biomedical research, methods that can handle reasoning and learning under uncertainty are becoming more and more important. The ongoing developments of the past two decades in the field of artificial intelligence have made it now possible to apply probabilistic methods to solve problems in real-world biomedical domains. Many representations have been suggested for solving problems in biomedical domains. Bayesian networks and influence diagrams have proved themselves useful for problems where probabilistic uncertainty is important, such as medical decision making and prognostics; logics have proved themselves useful in areas such as diagnosis. In recent years, the field of statistical relational learning has led to new formalisms which integrate probabilistic graphical models and logic. These formalisms provide exciting new opportunities for medical applications as they can be used to learn from structured medical data and reason with them using both logical and probabilistic methods. Another major theme for this workshop is in the handling of semantic concepts such as space and time in the biomedical domain. Space is an important concept when developing probabilistic models of, e.g., the spread of infectious disease, either in the hospital or in the community at large. Temporal reasoning is especially important in the context of personalised health care. Consider for example the translation of biomedical research that is expected to lead to more complex decision making, e.g., how to optimally select a sequence of drugs targeting biological pathways when treating a malignant tumour. There are strong expectations that such personalised and specific drugs will soon be available in the clinical practice. We selected eleven papers for full presentation. All these contributions fit the format of the workshop: they develop new approaches for integrating logical and semantical concepts with probabilistic methods or apply existing methods to problems from the biomedical domain. Furthermore, we feel honoured to have Jesse Davis and Milos Hauskrecht as invited speakers. Jesse Davis has made significant contributions in the application of statistical relational learning techniques in the medical domain. Milos Hauskrecht is well-known for his work in the analysis of time-series data (e.g., using POMDPs) in biomedical informatics. The organisers would like to acknowledge the support from the AIME organisation. We would also like to thank the program committee members for their support and reviewing, which have improved the accepted papers significantly.

Exploiting non-monotonic influences in qualitative belief networks

2000

In A qualitative belief network, dependences between variables are indicated by qual-itative signs These signs serve to model monotoniecprobabilistic relationships only: non-monotonic relationships between variables are modelled as lack of information. In this paper, we propose to include information about non-monotonic probabilistic influences between variables explicitly in a qualitative belief network We show that this information can be exploited in probabilistic inference to forestall unnecessarily weak results.

Zooming in on trade-offs in qualitative probabilistic networks

2000

Abstract Qualitative probabilistic networks have been designed for probabilistic reasoning in a qualitative way. As a consequence of their coarse level of representation detail, qualitative probabilistic networks do not provide for resolving trade-offs and typically yield ambiguous results upon inference. We present an algorithm for computing more informative results for unresolved trade-offs.

Using kappas as indicators of strength in qualitative probabilistic networks

2003

Qualitative probabilistic networks are designed for probabilistic inference in a qualitative way. They capture qualitative influences between variables, but do not provide for indicating the strengths of these influences. As a result, trade-offs between conflicting influences remain unresolved upon inference. In this paper, we investigate the use of order-of-magnitude kappa values to capture strengths of influences in a qualitative network. We detail the use of these kappas upon inference, thereby providing for trade-off resolution.

Introducing situational signs in qualitative probabilistic networks (original) (raw)

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