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555
Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences
 AMERICAN JOURNAL OF SOCIOLOGY
, 2005
"... A recursive analysis of network and institutional evolution is offered to account for the decentralized structure of the commercial field of the life sciences. Four alternative logics of attachment—accumulative advantage, homophily, followthetrend, and multiconnectivity—are tested to explain the s ..."
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Cited by 184 (8 self)
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A recursive analysis of network and institutional evolution is offered to account for the decentralized structure of the commercial field of the life sciences. Four alternative logics of attachment—accumulative advantage, homophily, followthetrend, and multiconnectivity—are tested to explain
Behavioral theories and the neurophysiology of reward,
 Annu. Rev. Psychol.
, 2006
"... ■ Abstract The functions of rewards are based primarily on their effects on behavior and are less directly governed by the physics and chemistry of input events as in sensory systems. Therefore, the investigation of neural mechanisms underlying reward functions requires behavioral theories that can ..."
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Cited by 187 (0 self)
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emotional states of anger, fear, and panic. NEUROPHYSIOLOGY OF REWARD BASED ON ANIMAL LEARNING THEORY Primary Reward Neurons responding to liquid or food rewards are found in a number of brain structures, such as orbitofrontal, premotor and prefrontal cortex, striatum, amygdala, and dopamine neurons
Robust independence testing for constraintbased learning of causal structure
 In UAI
, 2003
"... This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test of independence for constraintbased (CB) learning of causal structure. Our method produces a smoothed contingency table NX ..."
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Cited by 16 (1 self)
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This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test of independence for constraintbased (CB) learning of causal structure. Our method produces a smoothed contingency table
Bayesian Network Structure Learning by Recursive Autonomy Identification
, 2009
"... We propose the recursive autonomy identification (RAI) algorithm for constraintbased (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of conditional independence (CI) tests, edge direction and structure decomposition into autonomous substru ..."
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Cited by 10 (3 self)
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We propose the recursive autonomy identification (RAI) algorithm for constraintbased (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of conditional independence (CI) tests, edge direction and structure decomposition into autonomous substructures
Selfdetermination and persistence in a reallife setting: Toward a motivational model of high school dropout.
 Journal of Personality and Social Psychology,
, 1997
"... The purpose of this study was to propose and test a motivational model of high school dropout. The model posits that teachers, parents, and the school administration's behaviors toward students influence students' perceptions of competence and autonomy. The less autonomy supportive the so ..."
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Cited by 183 (19 self)
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school dropout using a prospective design and structural equation modeling. Overall, we believe that the present study should allow us to better understand the psychological processes involved in dropping out of high school as well as provide a test of intrinsicextrinsic motivation theory and research
Learning Bayesian Networks from Data: An InformationTheory Based Approach
, 2001
"... This paper provides algorithms that use an informationtheoretic analysis to learn Bayesian network structures from data. Based on our threephase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional indepe ..."
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Cited by 133 (4 self)
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This paper provides algorithms that use an informationtheoretic analysis to learn Bayesian network structures from data. Based on our threephase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional
Detecting marginal and conditional independencies between events and learning their causal structure
"... Abstract. Consider data given as a sequence of events, where each event has a timestamp and is of a specific type. We introduce a test for detecting marginal independence between events of two given types and for conditional independence when conditioned on one type. The independence test is based ..."
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and that the causal mechanisms depend on the event type. The causal structure is defined by a directed graph which may contain cycles. Based on the independence test, an algorithm is designed to uncover the causal structure. The results show many similarities with Bayesian network theory, except that the order
Causal discovery with continuous additive . . .
, 2014
"... We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural eq ..."
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Cited by 9 (3 self)
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We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural
Recursive Autonomy Identification for Bayesian Network Structure Learning
"... We propose a constraintbased algorithm for Bayesian network structure learning called recursive autonomy identification (RAI). The RAI algorithm learns the structure by recursive application of conditional independence (CI) tests of increasing orders, edge direction and structure decomposition into ..."
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We propose a constraintbased algorithm for Bayesian network structure learning called recursive autonomy identification (RAI). The RAI algorithm learns the structure by recursive application of conditional independence (CI) tests of increasing orders, edge direction and structure decomposition
Recursive Autonomy Identification for Bayesian Network Structure Learning
"... We propose a constraintbased algorithm for Bayesian network structure learning called recursive autonomy identification (RAI). The RAI algorithm learns the structure by recursive application of conditional independence (CI) tests of increasing orders, edge direction and structure decomposition into ..."
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We propose a constraintbased algorithm for Bayesian network structure learning called recursive autonomy identification (RAI). The RAI algorithm learns the structure by recursive application of conditional independence (CI) tests of increasing orders, edge direction and structure decomposition
Results 1  10
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555