154,473 research outputs found
Symbolic Exact Inference for Discrete Probabilistic Programs
The computational burden of probabilistic inference remains a hurdle for
applying probabilistic programming languages to practical problems of interest.
In this work, we provide a semantic and algorithmic foundation for efficient
exact inference on discrete-valued finite-domain imperative probabilistic
programs. We leverage and generalize efficient inference procedures for
Bayesian networks, which exploit the structure of the network to decompose the
inference task, thereby avoiding full path enumeration. To do this, we first
compile probabilistic programs to a symbolic representation. Then we adapt
techniques from the probabilistic logic programming and artificial intelligence
communities in order to perform inference on the symbolic representation. We
formalize our approach, prove it sound, and experimentally validate it against
existing exact and approximate inference techniques. We show that our inference
approach is competitive with inference procedures specialized for Bayesian
networks, thereby expanding the class of probabilistic programs that can be
practically analyzed
Ontology Merging as Social Choice
The problem of merging several ontologies has important applications in the Semantic Web, medical ontology engineering
and other domains where information from several distinct sources needs to be integrated in a coherent manner.We propose
to view ontology merging as a problem of social choice, i.e. as a problem of aggregating the input of a set of individuals
into an adequate collective decision. That is, we propose to view ontology merging as ontology aggregation. As a first step in
this direction, we formulate several desirable properties for ontology aggregators, we identify the incompatibility of some of
these properties, and we define and analyse several simple aggregation procedures. Our approach is closely related to work
in judgment aggregation, but with the crucial difference that we adopt an open world assumption, by distinguishing between
facts not included in an agent’s ontology and facts explicitly negated in an agent’s ontology
Multiple perspectives on the concept of conditional probability
Conditional probability is a key to the subjectivist theory of probability; however, it plays a subsidiary role in the usual conception of probability where its counterpart, namely independence is of basic importance. The paper investigates these concepts from various perspectives in order to shed light on their multi-faceted character. We will include the mathematical, philosophical, and educational perspectives. Furthermore, we will inspect conditional probability from the corners of competing ideas and solving strategies. For the comprehension of conditional probability, a wider approach is urgently needed to overcome the well-known problems in learning the concepts, which seem nearly unaffected by teaching
Semantic Criteria of Correct Formalization
This paper compares several models of formalization. It articulates criteria of correct formalization and identifies their problems. All of the discussed criteria are so called “semantic” criteria, which refer to the interpretation of logical formulas. However, as will be shown, different versions of an implicitly applied or explicitly stated criterion of correctness depend on different understandings of “interpretation” in this context
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview
We consider the problem of learning about and comparing the consequences of
dynamic treatment strategies on the basis of observational data. We formulate
this within a probabilistic decision-theoretic framework. Our approach is
compared with related work by Robins and others: in particular, we show how
Robins's 'G-computation' algorithm arises naturally from this
decision-theoretic perspective. Careful attention is paid to the mathematical
and substantive conditions required to justify the use of this formula. These
conditions revolve around a property we term stability, which relates the
probabilistic behaviours of observational and interventional regimes. We show
how an assumption of 'sequential randomization' (or 'no unmeasured
confounders'), or an alternative assumption of 'sequential irrelevance', can be
used to infer stability. Probabilistic influence diagrams are used to simplify
manipulations, and their power and limitations are discussed. We compare our
approach with alternative formulations based on causal DAGs or potential
response models. We aim to show that formulating the problem of assessing
dynamic treatment strategies as a problem of decision analysis brings clarity,
simplicity and generality.Comment: 49 pages, 15 figure
Forensic science expertise for international criminal proceedings: an old problem, a new context and a pragmatic resolution
Expert witness testimony provides an important source of information for international criminal proceedings, and forensic science expertise from mass graves is no exception: findings from exhumations and examinations have
featured in the ad hoc tribunals’ trials and judgments. Whilst the issues surrounding the law-science relationship have been explored within the realm of national legal systems, the mixed system adopted by these tribunals presents an established discussion with a new context. Using forensic archaeology as an example, this article explores some theoretical underpinnings and practical realities surrounding the use of forensic science during international criminal investigations into mass graves before looking at how Trial Chambers aim to establish the relevance and credibility of forensic science evidence. As little guidance regarding admissibility of expert evidence is provided, it is through the case-specific legal process of cross-examination and presentation of counter-expertise that methodological issues are resolved. This, together with reliance on normative principles, is the pragmatic approach adopted to discern reliability of expert opinion
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