Results 21 to 30 of about 43,440 (267)
Bayesian Models of Cognition [PDF]
, 2001 For over 200 years, philosophers and mathematicians have be en using probability theory to describe human cognition. While the theory of prob abilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty ...
L. Griffiths, Thomas +2 more
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Variational Bayesian image modelling [PDF]
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005 We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models---Hidden Markov Random Fields (HMRFs). HMRFs are particularly well suited to image modelling and in this paper, we apply them to the problem of image segmentation.
Li Cheng 0001 +3 more
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Dynamic causal modelling of COVID-19 and its mitigations
Scientific Reports, 2022 This technical report describes the dynamic causal modelling of mitigated epidemiological outcomes during the COVID-9 coronavirus outbreak in 2020. Dynamic causal modelling is a form of complex system modelling, which uses ‘real world’ timeseries to ...
Karl J. Friston +2 more
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Computational Statistics & Data Analysis, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Denison, D, Adams, N, Holmes, C, Hand, D
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A Bayesian Model of Information Cascades
, 2022 13 pages, 37 figures, Paper accepted for presentation A Bayesian model of information cascade in International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE), co-located with AAMAS ...
Sriashalya Srivathsan +2 more
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An efficient Bayesian modelling of extreme winds in the favour of energy generation in Pakistan
Energy Reports, 2023 Daily and annual maximum wind speed quantiles can be estimated using extreme value theory for any metrological site of interest. These estimates are of vast importance for modelling and predicting maximum wind speed.
Touqeer Ahmad +3 more
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A Bayesian Model for Chronic Pain
Frontiers in Pain Research, 2022 The perceiving mind constructs our coherent and embodied experience of the world from noisy, ambiguous and multi-modal sensory information. In this paper, we adopt the perspective that the experience of pain may similarly be the result of a probabilistic, inferential process.
Anna-Lena Eckert +2 more
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Mathematics, 2023 Bayesian networks are a powerful tool for modelling multivariate random variables. However, when applied in practice, for example, for industrial projects, problems arise because the existing learning and inference algorithms are not adapted to real data.
Irina Deeva +2 more
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Scientific Reports, 2023 Medieval southern Italy is typically viewed as a region where political, religious, and cultural systems coexisted and clashed. Written sources often focus on elites and give an image of a hierarchical feudal society supported by a farming economy.
Carlo Cocozza +11 more
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Bayesian unmasking in linear models [PDF]
Computational Statistics & Data Analysis, 2001 We propose a Bayesian procedure for multiple outlier detection in linear models avoiding the masking problem. Our proposal is illustrated with several examples in which our procedure outperforms other recent methods for multiple outlier detection. The posterior probabilities of each data point being an outlier are estimated by using a new adaptive ...
JUSTEL, Ana, PEÑA , Daniel
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