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Propositional and relational Bayesian networks associated with imprecise and qualitative probabilistic assessments
 IN PROCEEDINGS OF THE 20TH ANNUAL CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 2004
"... This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and firstorder constructs together with precise, imprecise, indeterminate and qualitative probabilistic as ..."
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Cited by 8 (4 self)
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This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and firstorder constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.
Automated design of operational transconductance amplifiers using reversed geometric programming
 In Proceedings of the 41th IEEE/ACM Design Automation Conference
, 2004
"... We present a method for designing operational amplifiers using reversed geometric programming, which is an extension of geometric programming that allows both convex and nonconvex constraints. Adding a limited set of nonconvex constraints can improve the accuracy of convex equationbased optimizati ..."
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We present a method for designing operational amplifiers using reversed geometric programming, which is an extension of geometric programming that allows both convex and nonconvex constraints. Adding a limited set of nonconvex constraints can improve the accuracy of convex equationbased optimization, without compromising global optimality. These constraints allow increased accuracy for critical modeling equations, such as the relationship between gm and IDS. To demonstrate the design methodology, a foldedcascode amplifier is designed in a 0.18 µm technology for varying speed requirements and is compared with simulations and designs obtained from geometric programming. Categories and Subject Descriptors:
Multilinear and Integer Programming for Markov Decision Processes with Imprecise Probabilities
"... Markov Decision Processes (MDPs) are extensively used to encode sequences of decisions with probabilistic effects. Markov Decision Processes with Imprecise Probabilities (MDPIPs) encode sequences of decisions whose effects are modeled using sets of probability distributions. In this paper we examine ..."
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Markov Decision Processes (MDPs) are extensively used to encode sequences of decisions with probabilistic effects. Markov Decision Processes with Imprecise Probabilities (MDPIPs) encode sequences of decisions whose effects are modeled using sets of probability distributions. In this paper we examine the computation of Γmaximin policies for MDPIPs using multilinear and integer programming. We discuss the application of our algorithms to “factored ” models and to a recent proposal, Markov Decision Processes with Setvalued Transitions (MDPSTs), that unifies the fields of probabilistic and “nondeterministic ” planning in artificial intelligence research.
Probabilistic Logic with Strong Independence
"... Abstract. This papers investigates the manipulation of statements of strong independence in probabilistic logic. Inference methods based on polynomial programming are presented for strong independence, both for unconditional and conditional cases. We also consider graphtheoretic representations, wh ..."
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Abstract. This papers investigates the manipulation of statements of strong independence in probabilistic logic. Inference methods based on polynomial programming are presented for strong independence, both for unconditional and conditional cases. We also consider graphtheoretic representations, where each node in a graph is associated with a Boolean variable and edges carry a Markov condition. The resulting model generalizes Bayesian networks, allowing probabilistic assessments and logical constraints to be mixed. 1
jpvQeecs.berkeley.edu
"... We present a method for designing operational amplifiers using reversed geometric programming, which is an extension of geometric programming that allows both convex and nonconvex constraints. Adding a limited set of nonconvex constraints can improve the accuracy of convex equationbased optimizati ..."
Abstract
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We present a method for designing operational amplifiers using reversed geometric programming, which is an extension of geometric programming that allows both convex and nonconvex constraints. Adding a limited set of nonconvex constraints can improve the accuracy of convex equationbased optimization, without compromising global optimality. These constraints allow increased accuracy for critical modeling equations, such as the relationship between gm and Ips. To demonstrate the design methodology, a foldedcascode amplifier is designed in a 0.18'pm technology for varying speed requirements and is compared with simnlations and designs obtained from geometric programming. Categories and Subject Descriptors:
PUCSP,
"... This papers investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among ..."
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This papers investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among random variables. Separation properties of graphical models are also investigated. 1
10.1 Automated Design of Operational Transconductance Amplifiers using Reversed Geometric Programming
"... We present a method for designing operational amplifiers using reversed geometric programming, which is an extension of geometric programming that allows both convex and nonconvex constraints. Adding a limited set of nonconvex constraints can improve the accuracy of convex equationbased optimizati ..."
Abstract
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We present a method for designing operational amplifiers using reversed geometric programming, which is an extension of geometric programming that allows both convex and nonconvex constraints. Adding a limited set of nonconvex constraints can improve the accuracy of convex equationbased optimization, without compromising global optimality. These constraints allow increased accuracy for critical modeling equations, such as the relationship between gm and IDS. To demonstrate the design methodology, a foldedcascode amplifier is designed in a 0.18 µm technology for varying speed requirements and is compared with simulations and designs obtained from geometric programming. Categories and Subject Descriptors:
Computing Lower and Upper Expectations under Epistemic Independence Abstract
"... This papers investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among ..."
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This papers investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among random variables. Separation properties of graphical models are also investigated. Key words: Sets of probability measures, concepts of independence, imprecise probabilities, epistemic independence, multilinear programming 1
4th International Symposium on Imprecise Probabilities and Their Applications, Pittsburgh, Pennsylvania, 2005 Computing Lower and Upper Expectations under Epistemic Independence
"... This papers investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among ..."
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This papers investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among random variables. Separation properties of graphical models are also investigated. 1