# probabilistic reasoning

**A constraint-propagation approach to probabilistic reasoning**

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Abstract The paper demonstrates that strict adherence to probability theory does not preclude the use of concurrent, self-activated constraint-propagation mechanisms for managing uncertainty. Maintaining local records of sources-of-belief allows both

**Qualitative propagation and scenario-based approaches to explanation of probabilistic reasoning**

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Abstract Comprehensible explanations of probabilistic reasoning are a prerequisite for wider acceptance of Bayesian methods in expert systems and decision support systems. A study of human reasoning under uncertainty suggests two different strategies for explaining

**Incidence calculus: A mechanism for probabilistic reasoning**

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Mechanisms for the automation of uncertainty are required for expert systems. Sometimes these mechanisms need to obey the properties of probabilistic reasoning. We argue that a purely numeric mechanism, like those proposed so far, cannot provide a probabilistic logic

**Probabilistic reasoning and certainty factors**

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The. development of automated assistance for medical diagnosis and decision making is an area of both theoretical and practical interest. Of methods for utilizing evidence to select diagnoses or decisions, probability theory has the firmest appeal. Probability theory in the

**Probabilistic reasoning with answer sets**

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We give a logic programming based account of probability and describe a declarative language P-log capable of reasoning which combines both logical and probabilistic arguments. Several non-trivial examples illustrate the use of P-log for knowledge

**Model-based probabilistic reasoning for electronics troubleshooting**

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ABSTRACT 1N-ATE is an on-going project aimed at developing expert consultant systems for guiding a novice technician through each step of an electronics troubleshooting session. One goal of the project is to automatically produce, given a set of initial symptoms, a

**Hierarchical bayesian networks: A probabilistic reasoning model for structured domains**

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Abstract Bayesian Networks are being used extensively for reasoning under uncertainty. Inference mechanisms for Bayesian Networks are compromised by the fact that they can only deal with propositional domains. In this work, we introduce an extension of that

**Probabilistic reasoning in decision support systems: from computation to common sense**

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This dissertation has been submitted to the Department of Engineering and Public Policy of Carnegie Mellon University on 21 January 1993 in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Partial support for this work was provided by the

**Resolving visual uncertainty and occlusion through probabilistic reasoning**

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Abstract Tracking interacting human body parts from a single two-dimensional view is difficult due to occlusion, ambiguity and spatio-temporal discontinuities. We present a Bayesian network method for this task. The method is not reliant upon spatio-temporal

**Non-monotonicity in probabilistic reasoning**

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ABSTRACT We start by defining an approach to non-monotonic probabiiistic reasoning in terms of non-monotonic categorical reasoning. We identify a type of non-monotonic probabilistic reasoning, akin to default inheritance, that seems to be commonly found in practice. We

**Towards a unifying theory of logical and probabilistic reasoning**

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ABSTRACT Logic and probability theory have both a long history in science. They are mainly rooted in philosophy and mathematics, but are nowadays important tools in many other fields such as computer science and, in particular, artificial intelligence. Some

**Children s probabilistic reasoning with a computer microworld**

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ABSTRACT This dissertation investigated children s probabilistic reasoning during a twomonth teaching experiment. As part of the research process, the researcher developed a computer microworld environment, Probability Explorer, for children s explorations with

**Mapping validation by probabilistic reasoning**

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In the semantic web environment, where several independent ontologies are used in order to describe knowledge and data, ontologies have to be aligned by defining mappings among the elements of one ontology and the elements of another ontology. Very often,

**Distributed multi-agent probabilistic reasoning with bayesian networks**

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Main stream approaches in distributed artificial intelligence (DAI) are essentially logic- based. Little has been reported to explore probabilistic approach in DAI. On the other hand, Bayesian networks have been applied to many AI tasks that require reasoning under

**Probabilistic logic under coherence, model-theoretic probabilistic logic, and default reasoning**

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Crucially, we even show that probabilistic reasoning under coherence is a proba- bilistic generalization of default reasoning in system P. That is, we provide a new probabilistic semantics for system P, which is neither based on infinitesimal prob- abilities nor on atomic-bound (or

**The AGILO robot soccer team-experience-based learning and probabilistic reasoning in autonomous robot control**

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This article describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it. According to our model the control system of an autonomous soccer robot consists of a probabilistic game state

**Logical generative models for probabilistic reasoning about existence, roles and identity**

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ABSTRACT In probabilistic reasoning, the problems of existence and identity are important to many different queries; for example, the probability that something that fits some description exists, the probability that some description refers to an object you know about or to a new

**Probabilistic semantics for nonmonotonic reasoning: A survey**

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2. Nonmonotonic Reasoning Viewed as Qualitative Probabilistic Reasoning. To those trained in traditional logics, symbolic reasoning is the standard, and nonmonotonicity a novelty . To stu- dents of

**Nonmonotonicity and human probabilistic reasoning**

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ABSTRACT Nonmonotonic reasoning allows-contrary to classical (monotone) logic-for withdrawing conclusions in the light of new evidence. Nonmonotonic reasoning is often claimed to mimic human common sense reasoning. Only a few studies, though, have

**Relational probabilistic conditional reasoning at maximum entropy**

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We also compare our approach to Bayesian logic programs (BLPs) from the field of statistical rela- tional learning which focuses on the combination of probabilistic reasoning and relational knowledge representation as well. 1 Introduction

**Probabilistic reasoning for large scale databases**

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ABSTRACT The complexity of probabilistic reasoning prohibits its application on a large scale of data. In order to reduce the complexity, implementations of modeling approaches restrict themselves with respect to expressive power or relax on the underlying probability theory.

**Probabilistic reasoning in preschoolers: Random sampling and base rate**

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ABSTRACT Recent research in cognitive and language development suggests that infants and young children are capable of complex computations and statistical inference. The present studies investigated whether 4-year-old children can solve simple probabilistic reasoning

**A method for temporal probabilistic reasoning**

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Temporal reasoning in complex environments often involves the interpretation of evidence under uncertainty. We have been studying the application of probabilistic reasoning techniques to the problem of temporaldata interpretation. In this report, we discuss

**Probabilistic reasoning for intelligent wind shear avoidance**

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A decision to avoid wind shear must be based on indirect meteorological evidence when accurate sensors to determine the presence of wind shear are unavailable. The Federal Avia-tion Administration (FAA) has provided guidelines for ?ight crews to help them

**Probabilistic reasoning with uncertain evidence**

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Page 1. Probabilistic reasoning with uncertain evidence Jiri Vomlel LISP V?SE Praha ´UTIA AV?CR Page 2. Probabilistic belief revision with new evidence • We conducted a study on smoking S and lung cancer C. • We get the counts n(S = s, C = c) for s, c ? {yes, no}.

**Lebesgue logic for probabilistic reasoning and some applications to perception**

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Reasoning with probabilities is essential to many sciences, such as decision theory, expert systems, neural networks, pattern recognition, and perception in general. In this paper we explore a new logic of probabilities, the Lebesgue logic, in which are defined the logical

**Probabilistic reasoning in bayesian networks: A relational database approach**

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Probabilistic reasoning in Bayesian networks is normally conducted on a junction tree by repeatedly applying the local propagation whenever new evidence is observed. In this paper, we suggest to treat probabilistic reasoning as database queries. We adapt a

**Probabilistic reasoning on object occurrence in complex scenes**

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Abstract The interpretation of complex scenes requires a large amount of prior knowledge and experience. To utilize prior knowledge in a computer vision or a decision support system for image interpretation, a probabilistic scene model for complex scenes is

**A comparison of two interval-valued probabilistic reasoning methods**

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Abstract Two complementary interval-valued probabilistic reasoning approaches, the incidence calculus proposed by Bundy and the cautious probabilistic reasoning method suggested by Quinlan, are analyzed and compared in this study. The correspondences

**Probabilistic reasoning in the semantic web using markov logic**

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MSc Thesis Probabilistic Reasoning in the Semantic Web using Markov Logic Page 2. 19 | 20 Pedro Carvalho de Oliveira| MSc Thesis Proposal Page 20. Probabilistic Reasoning in the Semantic Web using Markov Logic

**Using data, student experiences and collaboration in developing probabilistic reasoning at the introductory tertiary level**

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In the focus over the past decade on data-driven, realistic approaches to building statistical literacy and data analysis curriculum, the explicit development of probability reasoning beyond coins and dice has received less attention. There are two aspects of probability at

**Probabilistic default reasoning with conditional constraints**

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We present an approach to reasoning from statistical and subjective knowledge, which is based on a combination of probabilistic reasoning from conditional constraints with ap- proaches to default reasoning from conditional knowledge bases.

**Nonmonotonic probabilistic reasoning under variable-strength inheritance with overriding**

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We present new probabilistic generalizations of Pearl s entailment in System Z and Lehmann s lexicographic entailment, called Z?-and lex?-entailment, which are parameterized through a value [0, 1] that describes the strength of the inheritance of

**Probabilistic reasoning with terms**

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Abstract Many problems in artificial intelligence can be naturally approached by generating and manipulating probability distributions over structured objects. In this paper we represent structured objects by first-order logic terms (lists, trees, tuples, and nestings thereof) and

**Integrating probabilistic reasoning into a symbolic diagrammatic reasoner**

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ABSTRACT A key part of diagram understanding is the problem of glyph recognition. Glyph recognition is hard, because a glyph may be drawn in many different ways and with varying levels of precision. A diagrammatic reasoner must be able to recognize such glyphs. This

**Jumping to conclusions: A network model predicts schizophrenic patients performance on aprobabilistic reasoning task**

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Abstract This article extends computational models of schizophrenia that focus on the negative aspects of this syndrome to behavioral biases that are associated with a positive symptom of schizophrenia, namely delusions. The phenomenon studied is the jump-to-

**Unifying logical and probabilistic reasoning**

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Most formal techniques of automated reasoning are either rooted in logic or in probability theory. These areas have a long tradition in science, particularly among philosophers and mathematicians. More recently, computer scientists have discovered logic and probability

**Refining reasoning in qualitative probabilistic networks**

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Page 1. Re ning reasoning in qualitative probabilistic networks Simon Parsons Advanced Computation Laboratory Imperial Cancer Research Fund PO Box 123 Lincoln s Inn Fields London WC2A 3PX, UKAbstract In recent years

**Multi-modal reasoning medical diagnosis system integrated with probabilistic reasoning**

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ABSTRACT In this paper, a Multi Modal Reasoning (MMR) method integrated with probabilistic reasoning is proposed for the diagnosis support module of the open eHealth platform. MMR is based on both Rule Based Reasoning (RBR) and Case Based Reasoning (CBR). It is

**Probabilistic reasoning with hierarchically structured variables**

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ABSTRACT Many practical problems have random variables with a large number of values that can be hierarchically structured into anAbstraction tree of classes. This paper considers how to represent and exploit hierarchical structure in probabilistic reasoning. We represent the

**A DSL for Explaining Probabilistic Reasoning**

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We propose a new focus in language design where languages provide constructs that not only describe the computation of results, but also produce explanations of how and why those results were obtained. We posit that if users are to understand computations

**A proposal to combine probabilistic reasoning with case-based retrieval for software troubleshooting**

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ABSTRACT Analysis of a real case of software troubleshooting made it clear that probabilistic models, or Bayesian networks are suitable for modeling software troubleshooting processes in today s computing environments. As a way to support software troubleshooting by

**Probabilistic ABox reasoning: preliminary results**

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Probabilistic reasoning in terminological logics. In Proc. of Knowledge Representation-

**Constructing virtual sensors using probabilistic reasoning**

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Modern control systems and other monitoring systems require the acquisition of values of most of the parameters involved in the process. Examples of processes are industrial procedures or medical treatments or financial forecasts. However, sometimes some

**Efficient probabilistic reasoning in Bayes nets with mutual exclusion and context specific independence**

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Abstract Prior work has shown that context-specific independence (CSI) in Bayes networks can be exploited to speed up belief updating. We examine how networks with variables exhibiting mutual exclusion (eg selector variables), as well as CSI, can be efficiently

**A probabilistic interpretation for lazy nonmonotonic reasoning**

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Artificial Intelligence, 13: 27 ,39. Moore, RC 1985. Semantical Considerations on Nonmonotonic Logic Artificial Intelligence, 25: 75 ,94. Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. Pearl, J. 1989.

**Exploiting functional dependencies in qualitative probabilistic reasoning**

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Abstract Functional dependencies restrict the potential interactions among variables connected in a probabilistic network. This restriction can be exploited in qualitative probabilistic reasoning by introducing deterministic variables and modifying the inference

**Probabilistic reasoning in dynamic multiagent systems**

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ABSTRACT Probabilistic reasoning with multiply sectioned Bayesian networks (MSBNs) has been successfully applied in static domains under the cooperative multiagent paradigm. Probabilistic reasoning in dynamic domains under the same paradigm involves several

**Probabilistic reasoning through genetic algorithms and reinforcement learning**

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ABSTRACT In this paper, we develop an efficient approach for inferencing over Bayesian networks by using a reinforcement learning controller to direct a genetic algorithm. The random variables of a Bayesian network can be grouped into several sets reflecting the

**Causal and Probabilistic Reasoning in p-log**

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In this paper we give an overview of the knowledge representation (KR) language P-log [Baral, Gelfond, and Rushton 2009] whose design was greatly influenced by work of Judea Pearl. We introduce the syntax and semantics of P-log, give a number of examples of its

**Incorporating concept ontology to enable probabilistic concept reasoning for multi-level image annotation**

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Page 1. Incorporating Concept Ontology to Enable Probabilistic Concept Reasoning for Multi-Level Image Annotation Yuli Gao Dept of Computer Science UNC-Charlotte

**Using the probabilistic logic programming language P-log for causal and counterfactual reasoning and non-naive conditioning**

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ABSTRACT P-log is a probabilistic logic programming lan- guage, which combines both logic C. Baral, M. Gelfond, and N. Rushton. Probabilistic reasoning with answer sets.

**Modelling default and likelihood reasoning as probabilistic reasoning**

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This paper presents a probabilistic analysis of plausible reasoning about defaults and about likelihood.Likely and by default are in fact treated as duals in the same sense as possibility and necessity. To model these four forms probabilistically, a logicQDP and

**Probabilistic Reasoning in Evolutionary Theory**

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ABSTRACT. We will sketch the probability concept mainly in evolutionary theory and referentially in statistical mechanics. In the classical world view, there has been thought that the probabilities appeared in the scientific context is interpreted as frequencies or

**A logic for inductive probabilistic reasoning**

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Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from 70% of As are Bs and a is

**Probabilistic reasoning with continuous constraints**

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ABSTRACT. Continuous constraint reasoning assumes the uncertainty of numerical variables within given bounds and propagates such knowledge through a network of constraints, reducing the uncertainty. In some problems there is also information about the plausibility

**An intelligent problem solving environment for designing explanation models and for diagnostic reasoning in probabilistic domains**

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Neapolitan, RE (19901. Probabilistic Reasoning in Expert Systems, New York: Wiley. Newell, A. (19901. Pezul, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Ir!['erence, San Mateo: Morgan Kaufinan (2nd ed.). Pearl, J. (1993).

**Massively parallel probabilistic reasoning with boltzmann machines**

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We present a method for mapping a given Bayesian network to a Boltzmann machine architecture, in the sense that the the updating process of the resulting Boltzmann machine model probably converges to a state which can be mapped back to a maximum a

**Assumption-based reasoning and probabilistic argumentation systems**

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Page 1. Assumption{Based Reasoning and Probabilistic Argumentation Systems* J. Kohlas and R. Haenni Institute of Informatics University of Fribourg Regina Mundi CH{1700 Fribourg

**A database tool to support probabilistic assumption-based reasoning in intelligence analysis**

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Page 1. -1- A DATABASE TOOL TO SUPPORT PROBABILISTIC ASSUMPTION- BASED REASONING IN INTELLIGENCE ANALYSIS Marvin S. Cohen Decision Science Consortium, Inc. Reston, VA 22091Abstract The Self

**Nonmonotonicity of Probabilistic Reasoning**

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In the last thirty years a substantial amount of work in Knowledge Representation and Reasoning concentrated on the development and understanding of logical languages with non-monotonic entailment relations [5]. Recall that an entailment relation is called non-

**Characterisation of Model Error for Charpy Impact Energy of Heat Treated Steels UsingProbabilistic Reasoning and a Gaussian Mixture Model**

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Bayes Theorem Applied to NN Weightings (w): where D={t 1, t 2,, t N} are target outputs, X={x 1, x 2,, x N} are inputs, w=[w 1, w 2,, w W] is the BNNweightings, p (w) is the prior pdf, p (D| w) is the likelihood, p (w| D) is the posterior pdf, and p (D) is a normalising factor•

**Developing and reasoning about probabilistic programs in pGCL**

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example. Just as for standard loops, we can deal with invariants and termination sep- arately: common sense suggests that the probabilistic reasoning should be an extension of standard reasoning, and indeed that is the case.

**Toward a Perception-Based Theory of Probabilistic Reasoning**

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Abstract The past two decades have witnessed a dramatic growth in the use of probability- based methods in a wide variety of applications centering on automation of decision-making in an environment of uncertainty and incompleteness of information.

**Domain knowledge acquisition and plan recognition by probabilistic reasoning**

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In this paper, a probabilistic framework for acquiring domain knowledge from heterogeneous corpora is introduced. The acquired information is used for intelligent human-computer interaction through the web. The application selected for the framework experimentation

**The emergence of probabilistic reasoning in very young infants**

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ABSTRACT How do people make such rich inferences from such sparse data? Recent research has explored this inferential ability by investigating probabilistic reasoning in infancy. For example, 8-and 11-month-old infants can make inferences from samples to populations

**Towards a toolbox for relational probabilistic knowledge representation, reasoning, and learning**

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18] and other formalisms for relational probabilistic knowledge representation such as logical Bayesian networks [7] and probabilistic relational models [9], as well as to use KReator as a testbed to evaluate other approaches for relational probabilistic reasoning under maximum

**Propositional Reasoning that Tracks Probabilistic Reasoning**

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ABSTRACT This paper concerns the extent to which uncertain propositional reasoning can track probabilistic reasoning, and addresses kinematic problems that extend the familiar Lottery paradox. An acceptance rule assigns to each Bayesian credal state pa propositional belief

**Reasoning about states of probabilistic sequential programs**

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Unlike most works on probabilistic reasoning about programs, we do not confuse possibility with probability: possible valuations may occur with zero probability. This is not a restriction and we can confuse the two, if desired, by adding an axiom to the proof system.

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