Results 11 to 20 of about 332,886 (299)
Parameter learning in production economies
Journal of Monetary Economics, 2018 We examine how parameter learning amplifies the impact of macroeconomic shocks on equity prices and quantities in a standard production economy where a representative agent has Epstein-Zin preferences. An investor observes technology shocks which follow a regime-switching process but does not know the underlying model parameters governing the short ...
Mykola Babiak, Roman Kozhan
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Bayesian Network Parameter Learning Method on Small Samples [PDF]
Jisuanji gongcheng, 2016 Maximum likelihood estimation is a classical and effective method for Bayesian network parameter learning on large samples,but it is not consistent when learning on small sample with little expertise.To address the issue,a novel method called TL-WMLE is ...
LI Zida,LIAO Shizhong
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Online Variational Filtering and Parameter Learning
CoRR, 2021 27 pages, 6 figures.
Andrew Campbell +3 more
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Parameter learning for relational Bayesian networks [PDF]
Proceedings of the 24th international conference on Machine learning, 2007 We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood function, and to use this likelihood graph to perform the necessary computations for a gradient ascent likelihood optimization procedure.
Jaeger, Manfred; id_orcid +1 more
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Learning Probabilistic Ontologies with Distributed Parameter Learning. [PDF]
, 2015 We consider the problem of learning both the structure and the parameters of Probabilistic Description Logics under DISPONTE. DISPONTE (DIstribution Semantics for Probabilistic ONTologiEs) adapts the distribution semantics for Probabilistic Logic Programming to Description Logics.
COTA, Giuseppe +4 more
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Structure Learning via Parameter Learning [PDF]
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014 A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR.
William Yang Wang +2 more
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Meta-Ensemble Parameter Learning
CoRR, 2022 Ensemble of machine learning models yields improved performance as well as robustness. However, their memory requirements and inference costs can be prohibitively high. Knowledge distillation is an approach that allows a single model to efficiently capture the approximate performance of an ensemble while showing poor scalability as demand for re ...
Zhengcong Fei +4 more
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Ensemble Learning for Mapper Parameter Optimization [PDF]
2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI), 2023 The Mapper algorithm is a technique from TDA used to create low-dimensional graph-based representations of high-dimensional data, proven effective in numerous exploratory data analysis tasks. The Mapper algorithm’s output depends on several user-chosen parameters, and selecting their values is a non-trivial choice, significantly narrowing its potential
Padraig Fitzpatrick +3 more
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A Study of Using Bethe/Kikuchi Approximation for Learning Directed Graphic Models
IEEE Access, 2021 This paper applies the variational methods to learn the parameters and the probability of evidence of directed graphic models (also known as Bayesian networks (BNs)) when data contains missing values.
Peng Lin, Martin Neil, Norman Fenton
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Bilevel Parameter Learning for Higher-Order Total Variation Regularisation Models. [PDF]
J Math Imaging Vis, 2017 We consider a bilevel optimisation approach for parameter learning in higher-order total variation image reconstruction models. Apart from the least squares cost functional, naturally used in bilevel learning, we propose and analyse an alternative cost ...
De Los Reyes JC +2 more
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