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Subject
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Neural networks 5 Neuronale Netze 5 artificial neural network 5 universal approximation theorem 5 Python 4 Theorie 4 Theory 4 deep learning 4 keras 4 machine learning 4 perceptron 4 supervised learning 4 tensorflow 4 Algorithm 3 Algorithmus 3 Artificial intelligence 3 Künstliche Intelligenz 3 Learning 3 Learning process 3 Lernen 3 Lernprozess 3 Anlageverhalten 1 Approximation theorem. 1 Asset Returns 1 Behavioural finance 1 Capital income 1 Capital market returns 1 Classification 1 Clustering 1 Deep Neural Networks 1 Deep neural networks 1 Finite number of players 1 Forecasting model 1 Inf-convolution 1 Investment Strategies 1 Kapitaleinkommen 1 Kapitalmarktrendite 1 Limit theorems 1 Machine Learning 1 Nash Equilibria \infty-dimensional strategy spaces 1
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Online availability
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Free 4 Undetermined 4
Type of publication
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Article 5 Book / Working Paper 4
Type of publication (narrower categories)
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Working Paper 4 Article in journal 3 Aufsatz in Zeitschrift 3 Arbeitspapier 2 Graue Literatur 2 Non-commercial literature 2
Language
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English 7 Undetermined 2
Author
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Askitas, Nikos 4 Burzoni, M. 1 Doldi, A. 1 Glycopantis, Dionysius 1 Hashorva, Enkelejd 1 McGhee, William A. 1 Monzio Compagnoni, E. 1 Muir, Allan 1 Tan, Zhongquan 1 Zhang, Liangliang 1
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Published in...
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CESifo Working Paper 1 CESifo working papers 1 Discussion paper series / IZA 1 Economic Theory 1 IZA Discussion Papers 1 Journal of mathematical finance 1 Quantitative finance 1 Stochastic Processes and their Applications 1 The journal of computational finance 1
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Source
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ECONIS (ZBW) 5 EconStor 2 RePEc 2
Showing 1 - 9 of 9
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A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code
Askitas, Nikos - 2024
pivotal Universal Approximation Theorem—an essential "existence theorem". The exposition extends to the algorithmic …
Persistent link: https://www.econbiz.de/10014567597
Saved in:
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A Hands-On Machine Learning Primer for Social Scientists: Math, Algorithms and Code
Askitas, Nikos - 2024
pivotal Universal Approximation Theorem—an essential ”existence theorem”. The exposition extends to the algorithmic …
Persistent link: https://www.econbiz.de/10015096855
Saved in:
Cover Image
A hands-on machine learning primer for social scientists : math, algorithms and code
Askitas, Nikos - 2024
pivotal Universal Approximation Theorem - an essential "existence theorem". The exposition extends to the algorithmic …
Persistent link: https://www.econbiz.de/10014535259
Saved in:
Cover Image
A hands-on machine learning primer for social scientists : math, algorithms and code
Askitas, Nikos - 2024
pivotal Universal Approximation Theorem - an essential ”existence theorem”. The exposition extends to the algorithmic …
Persistent link: https://www.econbiz.de/10015070152
Saved in:
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Risk sharing with deep neural networks
Burzoni, M.; Doldi, A.; Monzio Compagnoni, E. - In: Quantitative finance 24 (2024) 2, pp. 233-252
Persistent link: https://www.econbiz.de/10014551970
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An artificial neural network representation of the SABR stochastic volatility model
McGhee, William A. - In: The journal of computational finance 25 (2021) 2, pp. 1-27
Persistent link: https://www.econbiz.de/10012938882
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Asset return prediction via machine learning
Zhang, Liangliang - In: Journal of mathematical finance 9 (2019) 4, pp. 691-697
Persistent link: https://www.econbiz.de/10012433454
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Exact asymptotics and limit theorems for supremum of stationary χ-processes over a random interval
Tan, Zhongquan; Hashorva, Enkelejd - In: Stochastic Processes and their Applications 123 (2013) 8, pp. 2983-2998
Let {χk(t),t≥0} be a stationary χ-process with k degrees of freedom being independent of some non-negative random variable T. In this paper we derive the exact asymptotics of P{supt∈[0,T]χk(t)u} as u→∞ when T has a regularly varying tail with index λ∈[0,1). Three other novel...
Persistent link: https://www.econbiz.de/10011064947
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Nash equilibria in \infty-dimensional spaces: an approximation theorem
Muir, Allan; Glycopantis, Dionysius - In: Economic Theory 13 (1999) 3, pp. 743-751
We show, by employing a density result for probability measures, that in games with a finite number of players and \infty-dimensional pure strategy spaces Nash equilibria can be approximated by finite mixed strategies. Given 0, each player receives an expected utility payoff /2 close to his Nash...
Persistent link: https://www.econbiz.de/10005596713
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