Results 1 - 10 of 555

Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences

by Walter W. Powell, Douglas R. White, Kenneth W. Koput, Jason Owen-Smith - 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, follow-the-trend, and multiconnectivity—are tested to explain the s ..."
Abstract - Cited by 184 (8 self) - Add to MetaCart
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, follow-the-trend, and multiconnectivity—are tested to explain

Behavioral theories and the neurophysiology of reward,

by Wolfram Schultz - 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 ..."
Abstract - Cited by 187 (0 self) - Add to MetaCart
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 constraint-based learning of causal structure

by Denver Dash - 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 ..."
Abstract - Cited by 16 (1 self) - Add to MetaCart
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

by Raanan Yehezkel, Boaz Lerner , 2009
"... We propose the recursive autonomy identification (RAI) algorithm for constraint-based (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 sub-stru ..."
Abstract - Cited by 10 (3 self) - Add to MetaCart
We propose the recursive autonomy identification (RAI) algorithm for constraint-based (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 sub-structures

Self-determination and persistence in a real-life setting: Toward a motivational model of high school dropout.

by Robert J Vallerand , Michelle S Fbrtier , Frederic Guay - 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 ..."
Abstract - Cited by 183 (19 self) - Add to MetaCart
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 Information-Theory Based Approach

by Jie Cheng, Russell Greiner, Jonathan Kelly, David Bell, Weiru Liu , 2001
"... This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional indepe ..."
Abstract - Cited by 133 (4 self) - Add to MetaCart
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase 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

by Jan Lemeire , Stijn Meganck , Albrecht Zimmermann , Thomas Dhollander
"... 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 . . .

by Jonas Peters, Joris M. Mooij, Dominik Janzing , 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 ex-periments, from which data are often not available. We show that if the observational distribution follows a structural eq ..."
Abstract - Cited by 9 (3 self) - Add to MetaCart
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 ex-periments, 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

by unknown authors
"... We propose a constraint-based 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 constraint-based 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

by Raanan Yehezkel, Boaz Lerner
"... We propose a constraint-based 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 constraint-based 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 of 555