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Ridge-based method for finding curvilinear structures from noisy data

Pulkkinen, Seppo - In: Computational Statistics & Data Analysis 82 (2015) C, pp. 89-109

as sampling noise and background clutter. It is shown that ridge curves of the marginal density induced by the model can … be used to estimate the generating functions. Given a Gaussian kernel density estimate for the marginal density, ridge …–corrector algorithm for tracing the ridge curve set of such a density estimate is developed. Efficiency and robustness of the algorithm …

Persistent link: https://www.econbiz.de/10011117702

Context-Based Scene Understanding

Furht, Borko; Zolghadr, Esfandiar - In: International Journal of Multimedia Data Engineering … 7 (2016) 1, pp. 22-40

Context plays an important role in performance of object detection. There are two popular considerations in building context models for computer vision applications; type of context (semantic, spatial, scale) and scope of the relations (pairwise, high-order). In this paper, a new unified...

Persistent link: https://www.econbiz.de/10012046501

Theorising and strategising mith models : generative models of social enterprises

Seelos, Christian - In: International journal of entrepreneurial venturing 6 (2014) 1, pp. 6-21

Persistent link: https://www.econbiz.de/10010495067

Segmenting market structure from multi-channel clickstream data: a novel generative model

Qian, Yang; Jiang, Yuanchun; Du, Yanan; Sun, Jianshan; … - In: Electronic commerce research 20 (2020) 3, pp. 509-533

Persistent link: https://www.econbiz.de/10012287494

Breaking the sample size barrier in model-based reinforcement learning with a generative model

Li, Gen; Wei, Yuting; Chi, Yuejie; Chen, Yuxin - In: Operations research 72 (2024) 1, pp. 203-221

Persistent link: https://www.econbiz.de/10014505071

Dirichlet process log skew-normal mixture with a missing-at-random-covariate in insurance claim analysis

Kim, Minkun; Lindberg, David; Crane, Martin; … - In: Econometrics : open access journal 11 (2023) 4, pp. 1-32

In actuarial practice, the modeling of total losses tied to a certain policy is a nontrivial task due to complex distributional features. In the recent literature, the application of the Dirichlet process mixture for insurance loss has been proposed to eliminate the risk of model...

Persistent link: https://www.econbiz.de/10014507911

A general framework for portfolio construction based on generative models of asset returns

Cheng, Tuoyuan; Chen, Kan - In: The Journal of finance and data science : JFDS 9 (2023), pp. 1-21

In this paper, we present an integrated approach to portfolio construction and optimization, leveraging high-performance computing capabilities. We first explore diverse pairings of generative model forecasts and objective functions used for portfolio optimization, which are evaluated using...

Persistent link: https://www.econbiz.de/10014514017

Theorizing and strategizing with models: Generative models of business models

Seelos, Christian - IESE Business School, Universidad de Navarra - 2010

The ambiguity surrounding model-based science is exemplified by the proliferation of meanings of the term "business model". We argue that a clearer specification of the analytical, theoretical and ontological validity of models is an opportunity to learn about and understand complex...

Persistent link: https://www.econbiz.de/10008641469

Overlapping community detection using a generative model for networks

Wang, Zhenwen; Hu, Yanli; Xiao, Weidong; Ge, Bin - In: Physica A: Statistical Mechanics and its Applications 392 (2013) 20, pp. 5218-5230

Detecting overlapping communities is a challenging task in analyzing networks, where nodes may belong to more than one community. Many present methods optimize quality functions to extract the communities from a network. In this paper, we present a probabilistic method for detecting overlapping...

Persistent link: https://www.econbiz.de/10010742306

Generative models for functional data using phase and amplitude separation

Tucker, J. Derek; Wu, Wei; Srivastava, Anuj - In: Computational Statistics & Data Analysis 61 (2013) C, pp. 50-66

Constructing generative models for functional observations is an important task in statistical functional analysis. In general, functional data contains both phase (or x or horizontal) and amplitude (or y or vertical) variability. Traditional methods often ignore the phase variability and focus...

Persistent link: https://www.econbiz.de/10010617232

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