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Probabilistic logic and probabilistic networks
, 2008
"... While in principle probabilistic logics might be applied to solve a range of problems, in practice they are rarely applied at present. This is perhaps because they seem disparate, complicated, and computationally intractable. However, we shall argue in this programmatic paper that several approaches ..."
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Cited by 22 (16 self)
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While in principle probabilistic logics might be applied to solve a range of problems, in practice they are rarely applied at present. This is perhaps because they seem disparate, complicated, and computationally intractable. However, we shall argue in this programmatic paper that several approaches to probabilistic logic fit into a simple unifying framework: logically complex evidence can be used to associate probability intervals or probabilities with sentences. Specifically, we show in Part I that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question: the standard probabilistic semantics (which takes probability functions as models), probabilistic argumentation (which considers the probability of a hypothesis being a logical consequence of the available evidence), evidential probability (which handles reference classes and frequency data), classical statistical inference
Objective Bayesianism, Bayesian Conditionalisation
, 2008
"... Objective Bayesianism has been criticised on the grounds that objective Bayesian updating, which on a finite outcome space appeals to the maximum entropy principle, differs from Bayesian conditionalisation. The main task of this paper is to show that this objection backfires: the difference between ..."
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Cited by 11 (7 self)
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Objective Bayesianism has been criticised on the grounds that objective Bayesian updating, which on a finite outcome space appeals to the maximum entropy principle, differs from Bayesian conditionalisation. The main task of this paper is to show that this objection backfires: the difference between the two forms of updating reflects negatively on Bayesian conditionalisation rather than on objective Bayesian updating. The paper also reviews some existing criticisms and justifications of conditionalisation, arguing in particular that the diachronic Dutch book justification fails because diachronic Dutch book arguments are subject to a reductio: in certain circumstances one can Dutch book an agent however she changes her degrees of belief. One may also criticise objective Bayesianism on the grounds that its norms are not compulsory but voluntary, the result of a stance. It is argued that this second objection also misses the mark, since objective
Objective bayesian probabilistic logic
 Journal of Algorithms
"... This paper develops connections between objective Bayesian epistemology—which holds that the strengths of an agent’s beliefs should be representable by probabilities, should be calibrated with evidence of empirical probability, and should otherwise be equivocal—and probabilistic logic. After introdu ..."
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Cited by 7 (4 self)
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This paper develops connections between objective Bayesian epistemology—which holds that the strengths of an agent’s beliefs should be representable by probabilities, should be calibrated with evidence of empirical probability, and should otherwise be equivocal—and probabilistic logic. After introducing objective Bayesian epistemology over propositional languages, the formalism is extended to handle predicate languages. A rather general probabilistic logic is formulated and then given a natural semantics in terms of objective Bayesian epistemology. The machinery of objective Bayesian nets and objective credal nets is introduced and this machinery is applied to provide a calculus for probabilistic logic that meshes with the objective Bayesian semantics.
Objective Bayesianism with predicate languages. Synthese
, 2008
"... Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to firstorder logical languages. It is argued that the objective Bayesian should choose a probability func ..."
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Cited by 5 (5 self)
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Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to firstorder logical languages. It is argued that the objective Bayesian should choose a probability function, from all those that satisfy constraints imposed by background knowledge, that is closest to a particular frequencyinduced probability function which generalises the λ = 0 function of Carnap’s continuum of inductive methods.
Philosophies of probability
 Handbook of the Philosophy of Mathematics, Volume 4 of the Handbook of the Philosophy of Science
"... This chapter presents an overview of the major interpretations of probability followed by an outline of the objective Bayesian interpretation and a discussion of the key challenges it faces. I discuss the ramifications of interpretations of probability and objective Bayesianism for the philosophy of ..."
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Cited by 4 (2 self)
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This chapter presents an overview of the major interpretations of probability followed by an outline of the objective Bayesian interpretation and a discussion of the key challenges it faces. I discuss the ramifications of interpretations of probability and objective Bayesianism for the philosophy of mathematics in general.
Composite Intrusion Detection in Process Control Networks
, 2009
"... An intrusion detection ensemble, i.e. a set of diverse intrusion detection algorithms employed as a group, has been shown to outperform each one those diverse algorithms employed individually. Moving along this line, we have devised an intrusion detection ensemble that inspects network packets that ..."
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Cited by 2 (0 self)
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An intrusion detection ensemble, i.e. a set of diverse intrusion detection algorithms employed as a group, has been shown to outperform each one those diverse algorithms employed individually. Moving along this line, we have devised an intrusion detection ensemble that inspects network packets that flow across the process control network of a digitally controlled physical system such as a power plant. Such process control specific intrusion detection ensemble is comprised of a statistical anomaly intrusion detection algorithm called the EstimationInspection (EI) algorithm, a physical process aware specificationbased approach, a theory of deception for intrusion detection that we call mirage theory, and an alert fusion technique in the form of a Bayesian theory of confirmation. In this research we leverage evolutions of the content of specific locations in the random access memory (RAM) of control systems into means of characterizing the normalcy or abnormality of network traffic. The EI algorithm uses estimation methods from applied statistics and probability theory to estimate normal evolutions of RAM content. The physical process aware specificationbased approach defines normal evolutions of RAM content via specifications developed manually through expert knowledge. Mirage theory consistently introduces deceptive evolutions of RAM content, and hence employs communicating finite state machines to detect any deviations caused by malicious network packets. The alert fusion technique also leverages evolutions of RAM content to estimate the degrees to which network traffic normalcy and abnormality hypotheses are confirmed on evidence. In this dissertation we provide a detailed discussion of these intrusion detection algorithms along with a detailed discussion of the alert fusion technique. We also discuss an empirical testing of the proposed intrusion detection ensemble in a small testbed comprised of Linux PCbased control systems that resemble the process control environment of a power plant; and in the case of the EI algorithm, a probabilistic validation via stochastic activity networks with activitymarking oriented reward structures.
Contents
, 2006
"... Kyburg goes halfway towards objective Bayesianism. He accepts that frequencies constrain rational belief to an interval but stops short of isolating an optimal degree of belief within this interval. I examine the case ..."
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Kyburg goes halfway towards objective Bayesianism. He accepts that frequencies constrain rational belief to an interval but stops short of isolating an optimal degree of belief within this interval. I examine the case
Aggregating Judgements by Merging Evidence
"... The theory of belief revision and merging has recently been applied to judgement aggregation. In this article I argue that judgements are best aggregated by merging the evidence on which they are based, rather than by directly merging the judgements themselves. This leads to a threestep strategy fo ..."
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The theory of belief revision and merging has recently been applied to judgement aggregation. In this article I argue that judgements are best aggregated by merging the evidence on which they are based, rather than by directly merging the judgements themselves. This leads to a threestep strategy for judgement aggregation. First, merge the evidence bases of the various agents using some method of belief merging. Second, determine which degrees of belief one should adopt on the basis of this merged evidence base, by applying objective Bayesian theory. Third, determine which judgements are appropriate given these degrees of belief by applying a decisiontheoretic account of rational judgement formation.
DOI 10.1007/s112290079298y Objective Bayesianism with predicate languages
"... Abstract Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to firstorder logical languages. It is argued that the objective Bayesian should choose a probabi ..."
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Abstract Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to firstorder logical languages. It is argued that the objective Bayesian should choose a probability function, from all those that satisfy constraints imposed by background knowledge, that is closest to a particular frequencyinduced probability function which generalises the λ = 0 function of Carnap’s continuum of inductive methods.