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Search Results: 1 - 10 of 8297 matches for " Kevin Murphy "
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The social pillar of sustainable development: a literature review and framework for policy analysis
Kevin Murphy
Sustainability : Science, Practice and Policy , 2012,
Abstract: There is a need to develop a clearer understanding of what the social pillar of sustainable development means and how it relates to the environmental pillar. This article contributes to this process by presenting a conceptual framework that identifies four overarching social concepts and links them to environmental imperatives. These concepts are: public awareness, equity, participation, and social cohesion. The framework builds on concepts and policy objectives outlined in research on international sustainable development indicators and the social sustainability literature. The social pillar can be expanded to include environmental, international, and intergenerational dimensions. This framework can then be used to examine how states and organizations understand the social pillar and its environmental links.
A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables
Kevin Murphy
Computer Science , 2013,
Abstract: We show how to use a variational approximation to the logistic function to perform approximate inference in Bayesian networks containing discrete nodes with continuous parents. Essentially, we convert the logistic function to a Gaussian, which facilitates exact inference, and then iteratively adjust the variational parameters to improve the quality of the approximation. We demonstrate experimentally that this approximation is faster and potentially more accurate than sampling. We also introduce a simple new technique for handling evidence, which allows us to handle arbitrary distributions on observed nodes, as well as achieving a significant speedup in networks with discrete variables of large cardinality.
How the unintended consequences of organizational interventions complicate the assessment of economic utility
Kevin R. Murphy
Economics and Business Letters , 2012,
Abstract: Interventions in organizations (e.g., implementing new testing, training or leadership development programs) are likely to have a wide range of effects, some intended and some unintended. These outcomes are likely to unfold over time in a wide range oftrajectories. A multi-stakeholder, multivariate longitudinal perspective is suggested as a way of reflecting more broadly the range of effects of organizational interventions when estimating their financial impact.
Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
Mark Schmidt,Kevin Murphy
Computer Science , 2012,
Abstract: We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a directed graph that allows cycles. In addition to discussing inference and sampling with this representation, we give an exponential family parametrization that allows parameter estimation to be stated as a convex optimization problem; we also give a convex relaxation of the task of simultaneous parameter and structure learning using group l1-regularization. The model is evaluated on simulated data and intracellular flow cytometry data.
Bayesian structure learning using dynamic programming and MCMC
Daniel Eaton,Kevin Murphy
Computer Science , 2012,
Abstract: MCMC methods for sampling from the space of DAGs can mix poorly due to the local nature of the proposals that are commonly used. It has been shown that sampling from the space of node orders yields better results [FK03, EW06]. Recently, Koivisto and Sood showed how one can analytically marginalize over orders using dynamic programming (DP) [KS04, Koi06]. Their method computes the exact marginal posterior edge probabilities, thus avoiding the need for MCMC. Unfortunately, there are four drawbacks to the DP technique: it can only use modular priors, it can only compute posteriors over modular features, it is difficult to compute a predictive density, and it takes exponential time and space. We show how to overcome the first three of these problems by using the DP algorithm as a proposal distribution for MCMC in DAG space. We show that this hybrid technique converges to the posterior faster than other methods, resulting in more accurate structure learning and higher predictive likelihoods on test data.
Efficient inference in occlusion-aware generative models of images
Jonathan Huang,Kevin Murphy
Computer Science , 2015,
Abstract: We present a generative model of images based on layering, in which image layers are individually generated, then composited from front to back. We are thus able to factor the appearance of an image into the appearance of individual objects within the image --- and additionally for each individual object, we can factor content from pose. Unlike prior work on layered models, we learn a shape prior for each object/layer, allowing the model to tease out which object is in front by looking for a consistent shape, without needing access to motion cues or any labeled data. We show that ordinary stochastic gradient variational bayes (SGVB), which optimizes our fully differentiable lower-bound on the log-likelihood, is sufficient to learn an interpretable representation of images. Finally we present experiments demonstrating the effectiveness of the model for inferring foreground and background objects in images.
The Factored Frontier Algorithm for Approximate Inference in DBNs
Kevin Murphy,Yair Weiss
Computer Science , 2013,
Abstract: The Factored Frontier (FF) algorithm is a simple approximate inferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similar tothe fully factorized version of the Boyen-Koller (BK) algorithm, butinstead of doing an exact update at every step followed bymarginalisation (projection), it always works with factoreddistributions. Hence it can be applied to models for which the exactupdate step is intractable. We show that FF is equivalent to (oneiteration of) loopy belief propagation (LBP) on the original DBN, andthat BK is equivalent (to one iteration of) LBP on a DBN where wecluster some of the nodes. We then show empirically that byiterating, LBP can improve on the accuracy of both FF and BK. Wecompare these algorithms on two real-world DBNs: the first is a modelof a water treatment plant, and the second is a coupled HMM, used tomodel freeway traffic.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012)
Nando de Freitas,Kevin Murphy
Computer Science , 2013,
Abstract: This is the Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, which was held on Catalina Island, CA August 14-18 2012.
Treatment of moderate to severe asthma: patient perspectives on combination inhaler therapy and implications for adherence
Kevin R Murphy, Bruce G Bender
Journal of Asthma and Allergy , 2009, DOI: http://dx.doi.org/10.2147/JAA.S4214
Abstract: eatment of moderate to severe asthma: patient perspectives on combination inhaler therapy and implications for adherence Review (7477) Total Article Views Authors: Kevin R Murphy, Bruce G Bender Published Date July 2009 Volume 2009:2 Pages 63 - 72 DOI: http://dx.doi.org/10.2147/JAA.S4214 Kevin R Murphy,1 Bruce G Bender2 1Allergy, Asthma and Pulmonary Research, Boys Town National Research Hospital, Omaha, Nebraska, USA; 2Division of Pediatric Behavioral Health, National Jewish Health, Denver, Colorado, USA Abstract: Symptom control in patients with moderate to severe persistent asthma is essential to reduce the significant morbidity associated with the disease. Poor adherence to controller medications has been identified as a major contributing factor to the high level of uncontrolled asthma. This review examines patient perspectives on, and preferences for, controller medications (inhaled corticosteroid and long-acting β2-agonist combinations [ICS/LABA]), and how this may affect adherence to therapy. Fluticasone/salmeterol and budesonide/formoterol, the currently available ICS/LABA combination products, have similar efficacy and tolerability based on a recent meta-analysis of asthma trials. Adherence is higher with the combination ICS/LABAs than when the components are administered separately. Investigations into patient preferences for desirable attributes of asthma medications indicate that an effective reliever with a fast onset and long duration of action is preferred and may lead to improved adherence. This rapid onset of effect was perceived and highly valued in patient surveys, and was associated with greater patient satisfaction. Thus, future research should be directed at therapy that offers both anti-inflammatory activity and a rapid onset of bronchodilator effect. To further improve patient adherence and treatment outcome, the effect of these characteristics as well as other factors on adherence should also be investigated.
Web Intelligence in Information Retrieval
Kevin Curran,Cliona Murphy,Stephen Annesley
Information Technology Journal , 2004,
Abstract: Web intelligence is a fascinating area in the very early stages of research and development. It combines the interaction of the human mind and artificial intelligence with networks and technology. How will the next generation Web mature? With the imminent growth of web intelligence what expectations do users have? Clearly the user will expect more from the Web than for it to merely pass raw data between people via search engines. This study attempts to define and summarise the concept of web intelligence, highlight the key elements of web intelligence and explore the topic of web information retrieval with particular focus on multimedia/information retrieval and intelligent agents.
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