Relevant bibliographies by topics / Bayes Modelling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bayes Modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Bayes Modelling"

1

Canale, A., and D. B. Dunson. "Nonparametric Bayes modelling of count processes." Biometrika 100, no. 4 (October 5, 2013): 801–16. http://dx.doi.org/10.1093/biomet/ast037.

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Schulte, Oliver, Hassan Khosravi, Arthur E. Kirkpatrick, Tianxiang Gao, and Yuke Zhu. "Modelling relational statistics with Bayes Nets." Machine Learning 94, no. 1 (May 1, 2013): 105–25. http://dx.doi.org/10.1007/s10994-013-5362-7.

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Verrall, R. J. "Bayes and Empirical Bayes Estimation for the Chain Ladder Model." ASTIN Bulletin 20, no. 2 (November 1990): 217–43. http://dx.doi.org/10.2143/ast.20.2.2005444.

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AbstractThe subject of predicting outstanding claims on a porfolio of general insurance policies is approached via the theory of hierarchical Bayesian linear models. This is particularly appropriate since the chain ladder technique can be expressed in the form of a linear model. The statistical methods which are applied allow the practitioner to use different modelling assumptions from those implied by a classical formulation, and to arrive at forecasts which have a greater degree of inherent stability. The results can also be used for other linear models. By using a statistical structure, a sound approach to the chain ladder technique can be derived. The Bayesian results allow the input of collateral information in a formal manner. Empirical Bayes results are derived which can be interpreted as credibility estimates. The statistical assumptions which are made in the modelling procedure are clearly set out and can be tested by the practitioner. The results based on the statistical theory form one part of the reserving procedure, and should be followed by expert interpretation and analysis. An illustration of the use of Bayesian and empirical Bayes estimation methods is given.
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Wibawa, Aji Prasetya, Yana Ningtyas, Nimas Hadi Atmaja, Ilham Ari Elbaith Zaeni, Agung Bella Putra Utama, Felix Andika Dwiyanto, and Andrew Nafalski. "Modelling Naïve Bayes for Tembang Macapat Classification." Harmonia: Journal of Arts Research and Education 22, no. 1 (July 1, 2022): 28–36. http://dx.doi.org/10.15294/harmonia.v22i1.34776.

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The tembang macapat can be classified using its cultural concepts of guru lagu, guru wilangan, and guru gatra. People may face difficulties recognizing certain songs based on the established rules. This study aims to build classification models of tembang macapat using a simple yet powerful Naïve Bayes classifier. The Naive Bayes can generate high-accuracy values from sparse data. This study modifies the concept of Guru Lagu by retrieving the last vowel of each line. At the same time, guru wilangan’s guidelines are amended by counting the number of all characters (Model 2) rather than calculating the number of syllables (Model 1). The data source is serat wulangreh with 11 types of tembang macapat, namely maskumambang, mijil, sinom, durma, asmaradana, kinanthi, pucung, gambuh, pangkur, dandhanggula, and megatruh. The k-fold cross-validation is used to evaluate the performance of 88 data. The result shows that the proposed Model 1 performs better than Model 2 in macapat classification. This promising method opens the potential of using a data mining classification engine as cultural teaching and preservation media.
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Boškoski, Pavle, Matija Perne, Martina Rameša, and Biljana Mileva Boshkoska. "Variational Bayes survival analysis for unemployment modelling." Knowledge-Based Systems 229 (October 2021): 107335. http://dx.doi.org/10.1016/j.knosys.2021.107335.

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Durante, D., and D. B. Dunson. "Nonparametric Bayes dynamic modelling of relational data." Biometrika 101, no. 4 (October 8, 2014): 883–98. http://dx.doi.org/10.1093/biomet/asu040.

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Shimoda, Yuko, and George B. Arhonditsis. "Integrating hierarchical Bayes with phosphorus loading modelling." Ecological Informatics 29 (September 2015): 77–91. http://dx.doi.org/10.1016/j.ecoinf.2015.07.005.

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Maes, M. A., M. R. Dann, and A. K. Midtgaard. "Spatial Hazard Rate Modelling Using Hierarchical Bayes Methods." Australian Journal of Structural Engineering 9, no. 1 (January 2009): 45–54. http://dx.doi.org/10.1080/13287982.2009.11465009.

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Lange, Nicholas. "Graphs and stochastic relaxation for hierarchical bayes modelling." Statistics in Medicine 11, no. 14-15 (1992): 2001–16. http://dx.doi.org/10.1002/sim.4780111417.

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Haidar, Ahmad, Elizabeth Potocka, Benoit Boulet, A. Margot Umpleby, and Roman Hovorka. "Estimating postprandial glucose fluxes using hierarchical Bayes modelling." Computer Methods and Programs in Biomedicine 108, no. 1 (October 2012): 102–12. http://dx.doi.org/10.1016/j.cmpb.2012.01.010.

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Dissertations / Theses on the topic "Bayes Modelling"

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Dowman, Mike. "Colour Terms, Syntax and Bayes Modelling Acquisition and Evolution." Thesis, The University of Sydney, 2004. http://hdl.handle.net/2123/558.

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This thesis investigates language acquisition and evolution, using the methodologies of Bayesian inference and expression-induction modelling, making specific reference to colour term typology, and syntactic acquisition. In order to test Berlin and Kay's (1969) hypothesis that the typological patterns observed in basic colour term systems are produced by a process of cultural evolution under the influence of universal aspects of human neurophysiology, an expression-induction model was created. Ten artificial people were simulated, each of which was a computational agent. These people could learn colour term denotations by generalizing from examples using Bayesian inference, and the resulting denotations had the prototype properties characteristic of basic colour terms. Conversations between these people, in which they learned from one-another, were simulated over several generations, and the languages emerging at the end of each simulation were investigated. The proportion of colour terms of each type correlated closely with the equivalent frequencies found in the World Colour Survey, and most of the emergent languages could be placed on one of the evolutionary trajectories proposed by Kay and Maffi (1999). The simulation therefore demonstrates how typological patterns can emerge as a result of learning biases acting over a period of time. Further work applied the minimum description length form of Bayesian inference to modelling syntactic acquisition. The particular problem investigated was the acquisition of the dative alternation in English. This alternation presents a learnability paradox, because only some verbs alternate, but children typically do not receive reliable evidence indicating which verbs do not participate in the alternation (Pinker, 1989). The model presented in this thesis took note of the frequency with which each verb occurred in each subcategorization, and so was able to infer which subcategorizations were conspicuously absent, and so presumably ungrammatical. Crucially, it also incorporated a measure of grammar complexity, and a preference for simpler grammars, so that more general grammars would be learned unless there was sufficient evidence to support the incorporation of some restriction. The model was able to learn the correct subcategorizations for both alternating and non-alternating verbs, and could generalise to allow novel verbs to appear in both constructions. When less data was observed, it also overgeneralized the alternation, which is a behaviour characteristic of children when they are learning verb subcategorizations. These results demonstrate that the dative alternation is learnable, and therefore that universal grammar may not be necessary to account for syntactic acquisition. Overall, these results suggest that the forms of languages may be determined to a much greater extent by learning, and by cumulative historical changes, than would be expected if the universal grammar hypothesis were correct.
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Dowman, Mike. "Colour Terms, Syntax and Bayes Modelling Acquisition and Evolution." University of Sydney. Information Technologies, 2004. http://hdl.handle.net/2123/558.

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This thesis investigates language acquisition and evolution, using the methodologies of Bayesian inference and expression-induction modelling, making specific reference to colour term typology, and syntactic acquisition. In order to test Berlin and Kay�s (1969) hypothesis that the typological patterns observed in basic colour term systems are produced by a process of cultural evolution under the influence of universal aspects of human neurophysiology, an expression-induction model was created. Ten artificial people were simulated, each of which was a computational agent. These people could learn colour term denotations by generalizing from examples using Bayesian inference, and the resulting denotations had the prototype properties characteristic of basic colour terms. Conversations between these people, in which they learned from one-another, were simulated over several generations, and the languages emerging at the end of each simulation were investigated. The proportion of colour terms of each type correlated closely with the equivalent frequencies found in the World Colour Survey, and most of the emergent languages could be placed on one of the evolutionary trajectories proposed by Kay and Maffi (1999). The simulation therefore demonstrates how typological patterns can emerge as a result of learning biases acting over a period of time. Further work applied the minimum description length form of Bayesian inference to modelling syntactic acquisition. The particular problem investigated was the acquisition of the dative alternation in English. This alternation presents a learnability paradox, because only some verbs alternate, but children typically do not receive reliable evidence indicating which verbs do not participate in the alternation (Pinker, 1989). The model presented in this thesis took note of the frequency with which each verb occurred in each subcategorization, and so was able to infer which subcategorizations were conspicuously absent, and so presumably ungrammatical. Crucially, it also incorporated a measure of grammar complexity, and a preference for simpler grammars, so that more general grammars would be learned unless there was sufficient evidence to support the incorporation of some restriction. The model was able to learn the correct subcategorizations for both alternating and non-alternating verbs, and could generalise to allow novel verbs to appear in both constructions. When less data was observed, it also overgeneralized the alternation, which is a behaviour characteristic of children when they are learning verb subcategorizations. These results demonstrate that the dative alternation is learnable, and therefore that universal grammar may not be necessary to account for syntactic acquisition. Overall, these results suggest that the forms of languages may be determined to a much greater extent by learning, and by cumulative historical changes, than would be expected if the universal grammar hypothesis were correct.
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Revie, Matthew. "Evaluation of bayes linear modelling to support reliability assessment during procurement." Thesis, University of Strathclyde, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487866.

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A major task facing a number of different procuring agencies is the assessment of the developmental reliability of a product as contractors incrementally provide information. These agencies would like to use structured quantitative methodologies, as opposed to unstructured assessment currently adopted, to evaluate reliability throughout the life of a development programme as additional information becomes available. Due to resource constraints, any developed methodology must be cost and time efficient. This research attempts to develop a methodology for customers that is capable of assessing the information presented by a contractor in a reliability case to support decision making throughout the procurement process.
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Starobinskaya, Irina. "Structural modelling of operational risk in financial institutions : application of Bayesian networks and balanced scorecards to IT infrastructure risk modelling /." Berlin : Pro Business, 2008. http://d-nb.info/991725328/04.

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Steinberg, Daniel. "An Unsupervised Approach to Modelling Visual Data." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9415.

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For very large visual datasets, producing expert ground-truth data for training supervised algorithms can represent a substantial human effort. In these situations there is scope for the use of unsupervised approaches that can model collections of images and automatically summarise their content. The primary motivation for this thesis comes from the problem of labelling large visual datasets of the seafloor obtained by an Autonomous Underwater Vehicle (AUV) for ecological analysis. It is expensive to label this data, as taxonomical experts for the specific region are required, whereas automatically generated summaries can be used to focus the efforts of experts, and inform decisions on additional sampling. The contributions in this thesis arise from modelling this visual data in entirely unsupervised ways to obtain comprehensive visual summaries. Firstly, popular unsupervised image feature learning approaches are adapted to work with large datasets and unsupervised clustering algorithms. Next, using Bayesian models the performance of rudimentary scene clustering is boosted by sharing clusters between multiple related datasets, such as regular photo albums or AUV surveys. These Bayesian scene clustering models are extended to simultaneously cluster sub-image segments to form unsupervised notions of “objects” within scenes. The frequency distribution of these objects within scenes is used as the scene descriptor for simultaneous scene clustering. Finally, this simultaneous clustering model is extended to make use of whole image descriptors, which encode rudimentary spatial information, as well as object frequency distributions to describe scenes. This is achieved by unifying the previously presented Bayesian clustering models, and in so doing rectifies some of their weaknesses and limitations. Hence, the final contribution of this thesis is a practical unsupervised algorithm for modelling images from the super-pixel to album levels, and is applicable to large datasets.
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Jones, Matthew James. "Bayes linear strategies for the approximation of complex numerical calculations arising in sequential design and physical modelling problems." Thesis, Durham University, 2017. http://etheses.dur.ac.uk/12529/.

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In a range of different scientific fields, deterministic calculations for which there is no analytic solution must be approximated numerically. The use of numerical approximations is necessary, but introduces a discrepancy between the true solution and the numerical solution that is generated. Bayesian methods are used to account for uncertainties introduced through numerical approximation in a variety of situations. To solve problems in Bayesian sequential experimental design, a sequence of complex integration and optimisation steps must be performed; for most problems, these calculations have no closed-form solution. An approximating framework is developed which tracks numerical uncertainty about the result of each calculation through each step of the design procedure. This framework is illustrated through application to a simple linear model, and to a more complex problem in atmospheric dispersion modelling. The approximating framework is also adapted to allow for the situation where beliefs about a model may change at certain points in the future. Where ordinary or partial differential equation (ODE or PDE) systems are used to represent a real-world system, it is rare that these can be solved directly. A wide variety of different approximation strategies have been developed for such problems; the approximate solution that is generated will differ from the true solution in some unknown way. A Bayesian framework which accounts for the uncertainty induced through numerical approximation is developed, and Bayes linear graphical analysis is used to efficiently update beliefs about model components using observations on the real system. In the ODE case, the framework is illustrated through application to a Lagrangian mechanical model for the interaction between a set of ringing bells and the tower in which they are hung; in the PDE case, the framework is illustrated through application to the heat equation in one spatial dimension.
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Salge, Christoph. "Information theoretic models of social interaction." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/13887.

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This dissertation demonstrates, in a non-semantic information-theoretic framework, how the principles of 'maximisation of relevant information' and 'information parsimony' can guide the adaptation of an agent towards agent-agent interaction. Central to this thesis is the concept of digested information; I argue that an agent is intrinsically motivated to a.) process the relevant information in its environment and b.) display this information in its own actions. From the perspective of similar agents, who require similar information, this differentiates other agents from the rest of the environment, by virtue of the information they provide. This provides an informational incentive to observe other agents and integrate their information into one's own decision making process. This process is formalized in the framework of information theory, which allows for a quantitative treatment of the resulting effects, specifically how the digested information of an agent is influenced by several factors, such as the agent's performance and the integrated information of other agents. Two specific phenomena based on information maximisation arise in this thesis. One is flocking behaviour similar to boids that results when agents are searching for a location in a girdworld and integrated the information in other agent's actions via Bayes' Theorem. The other is an effect where integrating information from too many agents becomes detrimental to an agent's performance, for which several explanations are provided.
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Baker, Peter John. "Applied Bayesian modelling in genetics." Thesis, Queensland University of Technology, 2001.

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Jaradat, Shatha. "OLLDA: Dynamic and Scalable Topic Modelling for Twitter : AN ONLINE SUPERVISED LATENT DIRICHLET ALLOCATION ALGORITHM." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177535.

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Providing high quality of topics inference in today's large and dynamic corpora, such as Twitter, is a challenging task. This is especially challenging taking into account that the content in this environment contains short texts and many abbreviations. This project proposes an improvement of a popular online topics modelling algorithm for Latent Dirichlet Allocation (LDA), by incorporating supervision to make it suitable for Twitter context. This improvement is motivated by the need for a single algorithm that achieves both objectives: analyzing huge amounts of documents, including new documents arriving in a stream, and, at the same time, achieving high quality of topics’ detection in special case environments, such as Twitter. The proposed algorithm is a combination of an online algorithm for LDA and a supervised variant of LDA - labeled LDA. The performance and quality of the proposed algorithm is compared with these two algorithms. The results demonstrate that the proposed algorithm has shown better performance and quality when compared to the supervised variant of LDA, and it achieved better results in terms of quality in comparison to the online algorithm. These improvements make our algorithm an attractive option when applied to dynamic environments, like Twitter. An environment for analyzing and labelling data is designed to prepare the dataset before executing the experiments. Possible application areas for the proposed algorithm are tweets recommendation and trends detection.
Tillhandahålla högkvalitativa ämnen slutsats i dagens stora och dynamiska korpusar, såsom Twitter, är en utmanande uppgift. Detta är särskilt utmanande med tanke på att innehållet i den här miljön innehåller korta texter och många förkortningar. Projektet föreslår en förbättring med en populär online ämnen modellering algoritm för Latent Dirichlet Tilldelning (LDA), genom att införliva tillsyn för att göra den lämplig för Twitter sammanhang. Denna förbättring motiveras av behovet av en enda algoritm som uppnår båda målen: analysera stora mängder av dokument, inklusive nya dokument som anländer i en bäck, och samtidigt uppnå hög kvalitet på ämnen "upptäckt i speciella fall miljöer, till exempel som Twitter. Den föreslagna algoritmen är en kombination av en online-algoritm för LDA och en övervakad variant av LDA - Labeled LDA. Prestanda och kvalitet av den föreslagna algoritmen jämförs med dessa två algoritmer. Resultaten visar att den föreslagna algoritmen har visat bättre prestanda och kvalitet i jämförelse med den övervakade varianten av LDA, och det uppnådde bättre resultat i fråga om kvalitet i jämförelse med den online-algoritmen. Dessa förbättringar gör vår algoritm till ett attraktivt alternativ när de tillämpas på dynamiska miljöer, som Twitter. En miljö för att analysera och märkning uppgifter är utformad för att förbereda dataset innan du utför experimenten. Möjliga användningsområden för den föreslagna algoritmen är tweets rekommendation och trender upptäckt.
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Wang, David I.-Chung. "Speaker diarization : "who spoke when"." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/59624/1/David_Wang_Thesis.pdf.

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Speaker diarization is the process of annotating an input audio with information that attributes temporal regions of the audio signal to their respective sources, which may include both speech and non-speech events. For speech regions, the diarization system also specifies the locations of speaker boundaries and assign relative speaker labels to each homogeneous segment of speech. In short, speaker diarization systems effectively answer the question of ‘who spoke when’. There are several important applications for speaker diarization technology, such as facilitating speaker indexing systems to allow users to directly access the relevant segments of interest within a given audio, and assisting with other downstream processes such as summarizing and parsing. When combined with automatic speech recognition (ASR) systems, the metadata extracted from a speaker diarization system can provide complementary information for ASR transcripts including the location of speaker turns and relative speaker segment labels, making the transcripts more readable. Speaker diarization output can also be used to localize the instances of specific speakers to pool data for model adaptation, which in turn boosts transcription accuracies. Speaker diarization therefore plays an important role as a preliminary step in automatic transcription of audio data. The aim of this work is to improve the usefulness and practicality of speaker diarization technology, through the reduction of diarization error rates. In particular, this research is focused on the segmentation and clustering stages within a diarization system. Although particular emphasis is placed on the broadcast news audio domain and systems developed throughout this work are also trained and tested on broadcast news data, the techniques proposed in this dissertation are also applicable to other domains including telephone conversations and meetings audio. Three main research themes were pursued: heuristic rules for speaker segmentation, modelling uncertainty in speaker model estimates, and modelling uncertainty in eigenvoice speaker modelling. The use of heuristic approaches for the speaker segmentation task was first investigated, with emphasis placed on minimizing missed boundary detections. A set of heuristic rules was proposed, to govern the detection and heuristic selection of candidate speaker segment boundaries. A second pass, using the same heuristic algorithm with a smaller window, was also proposed with the aim of improving detection of boundaries around short speaker segments. Compared to single threshold based methods, the proposed heuristic approach was shown to provide improved segmentation performance, leading to a reduction in the overall diarization error rate. Methods to model the uncertainty in speaker model estimates were developed, to address the difficulties associated with making segmentation and clustering decisions with limited data in the speaker segments. The Bayes factor, derived specifically for multivariate Gaussian speaker modelling, was introduced to account for the uncertainty of the speaker model estimates. The use of the Bayes factor also enabled the incorporation of prior information regarding the audio to aid segmentation and clustering decisions. The idea of modelling uncertainty in speaker model estimates was also extended to the eigenvoice speaker modelling framework for the speaker clustering task. Building on the application of Bayesian approaches to the speaker diarization problem, the proposed approach takes into account the uncertainty associated with the explicit estimation of the speaker factors. The proposed decision criteria, based on Bayesian theory, was shown to generally outperform their non- Bayesian counterparts.
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Books on the topic "Bayes Modelling"

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2010), European-Japanese Conference on Information Modelling and Knowledge Bases (20th. Information modelling and knowledge bases XXII. Amsterdam: Ios Press, 2011.

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European-Japanese Conference on Information Modelling and Knowledge Bases (18th 2008). Information modelling and knowledge bases XX. Amsterdam: IOS Press, 2009.

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Bech, Niels. Modelling of surface combustion straw bales. Roskilde: Risø National Laboratory, 1994.

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Hannu, Jaakkola, Kiyoki Yasushi, and Tokuda Takahiro, eds. Information modelling and knowledge bases XIX. Amsterdam: IOS Press, 2008.

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Tallinn, Estonia) 2005 European-Japanese Conference on Information Modelling and Knowledge Bases (15th. Proceedings of the 15th European-Japanese conference on information modelling and knowledge bases: May 16-19, 2005, Tallinn, Estonia. Tallinn: Tallinn University of Technology, Dept. of Informatics, 2005.

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Jaworski, Barbara, Josef Rebenda, Reinhard Hochmuth, Stephanie Thomas, Michèle Artigue, Inés Gómez-Chacón, Sarah Khellaf, et al. Inquiry in University Mathematics Teaching and Learning. Brno: Masaryk University Press, 2021. http://dx.doi.org/10.5817/cz.muni.m210-9983-2021.

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The book presents developmental outcomes from an EU Erasmus+ project involving eight partner universities in seven countries in Europe. Its focus is the development of mathematics teaching and learning at university level to enhance the learning of mathematics by university students. Its theoretical focus is inquiry-based teaching and learning. It bases all activity on a three-layer model of inquiry: (1) Inquiry in mathematics and in the learning of mathematics in lecture, tutorial, seminar or workshop, involving students and teachers; (2) Inquiry in mathematics teaching involving teachers exploring and developing their own practices in teaching mathematics; (3) Inquiry as a research process, analysing data from layers (1) and (2) to advance knowledge inthe field. As required by the Erasmus+ programme, it defines Intellectual Outputs (IOs) that will develop in the project. PLATINUM has six IOs: The Inquiry-based developmental model; Inquiry communities in mathematics learning and teaching; Design of mathematics tasks and teaching units; Inquiry-based professional development activity; Modelling as an inquiry process; Evalutation of inquiry activity with students. The project has developed Inquiry Communities, in each of the partner groups, in which mathematicians and educators work together in supportive collegial ways to promote inquiry processes in mathematics learning and teaching. Through involving students in inquiry activities, PLATINUM aims to encourage students` own in-depth engagement with mathematics, so that they develop conceptual understandings which go beyond memorisation and the use of procedures. Indeed the eight partners together have formed an inquiry community, working together to achieve PLATINUM goals within the specific environments of their own institutions and cultures. Together we learn from what we are able to achieve with respect to both common goals and diverse environments, bringing a richness of experience and learning to this important area of education. Inquiry communities enable participants to address the tensions and issues that emerge in developmental processes and to recognise the critical nature of the developmental process. Through engaging in inquiry-based development, partners are enabled and motivated to design activities for their peers, and for newcomers to university teaching of mathematics, to encourage their participation in new forms of teaching, design of teaching, and activities for students. Such professional development design is an important outcome of PLATINUM. One important area of inquiry-based activity is that of “modelling” in mathematics. Partners have worked together across the project to investigate the nature of modelling activities and their use with students. Overall, the project evaluates its activity in these various parts to gain insights to the sucess of inquiry based teaching, learning and development as well as the issues and tensions that are faced in putting into practice its aims and goals.
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International Symposium on Building Economics (4th 1987 Danish Building Research Institute, Copenhagen). Building economics: CIB proceedings of the fourth International Symposium on Building Economics. : Cost modelling techniques, cost/pricedata bases, design methods, optimization and other integrated automatic processes, buildability. Hørsholm: Danish Building Research Institute, 1987.

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Lee, Sik-Yum. Structural Equation Modelling: A Bayesian Approach (Wiley Series in Probability and Statistics). Wiley, 2007.

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Yu, Angela J. Bayesian Models of Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.025.

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Traditionally, attentional selection has been thought of as arising naturally from resource limitations, with a focus on what might be the most apt metaphor, e.g. whether it is a ‘bottleneck’ or ‘spotlight’. However, these simple metaphors cannot account for the specificity, flexibility, and heterogeneity of the way attentional selection manifests itself in different behavioural contexts. A recent body of theoretical work has taken a different approach, focusing on the computational needs of selective processing, relative to environmental constraints and behavioural goals. They typically adopt a normative computational framework, incorporating Bayes-optimal algorithms for information processing and action selection. This chapter reviews some of this recent modelling work, specifically in the context of attention for learning, covert spatial attention, and overt spatial attention.
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Endrjukaite, T., A. Dudko, and H. Jaakkola. Information Modelling and Knowledge Bases XXX. IOS Press, Incorporated, 2019.

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Book chapters on the topic "Bayes Modelling"

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Berger, James O., and Luis R. Pericchi. "On The Justification of Default and Intrinsic Bayes Factors." In Modelling and Prediction Honoring Seymour Geisser, 276–93. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2414-3_17.

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McCulloch, Robert E., and Peter E. Rossi. "Bayes Factors for Testing the Equality of Covariance Matrix Eigenvalues." In Modelling and Prediction Honoring Seymour Geisser, 305–14. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2414-3_19.

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Bozza, Silvia, Franco Taroni, and Alex Biedermann. "Bayes Factor for Evaluative Purposes." In Bayes Factors for Forensic Decision Analyses with R, 79–139. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09839-0_3.

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AbstractThis chapter presents and discusses the use of the Bayes factor for the evaluation of scientific evidence in the form of discrete, continuous and continuous multivariate data. The latter may present a complex dependence structure that will be handled by means of multilevel models. The notion of “evaluative purpose” is understood here as referring to situations in which material of known source (control material) and evidential material of unknown source (recovered or questioned material) is collected and analyzed. The purpose is to evaluate the effect of the output of the examinations, in the form of scores or measurements of features, on the odds in favor of a proposition put forward by the prosecution, compared to an alternative proposition advanced by the defence. A discussion is included of the sensitivity of the described Bayes factor procedures to changes in the features of recovered and control materials, the available background information, as well as to choices made during probabilistic modelling and prior elicitation.
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Schmidbauer, Harald, and Angi Rösch. "A Subjective Bayes Approach to the Queueing System M/E k /c." In Modelling and Decisions in Economics, 139–55. Heidelberg: Physica-Verlag HD, 1999. http://dx.doi.org/10.1007/978-3-662-12519-9_9.

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Kostyuchenko, Yuriy V., Maxim Yuschenko, Ivan Kopachevsky, and Igor Artemenko. "Bayes Decision-Making Systems for Quantitative Assessment of Hydrological Climate-Related Risk using Satellite Data." In Mathematical Modelling of System Resilience, 113–41. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338819-5.

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Stoliarova, Valerie, Gulnara Sadykova, Anna Olina, and Alexander Tulupyev. "The Non–parametric Bayes Belief Network for the Physical Activity Parameters Modelling: The Pilot Study." In Lecture Notes in Networks and Systems, 224–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87178-9_23.

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Viviani, P. "On Modelling." In Neural Bases of Motor Behaviour, 315–34. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-017-2403-6_12.

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Barry, Phillip J., and Ronald N. Goldman. "Unimodal Properties of Generalized Ball Bases." In Geometric Modelling, 35–41. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7584-2_2.

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Warren, J., and H. Weimer. "Radial Basis Functions, Discrete Differences, and Bell-Shaped Bases." In Geometric Modelling, 337–51. Vienna: Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6270-5_20.

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Ilievski, D., and E. T. White. "Modelling Bayer Precipitation with Agglomeration." In Essential Readings in Light Metals, 509–15. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48176-0_70.

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Conference papers on the topic "Bayes Modelling"

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Carlsson, Christer, Markku Heikkila, and Jozsef Mezei. "Possibilistic Bayes modelling for predictive analytics." In 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2014. http://dx.doi.org/10.1109/cinti.2014.7028671.

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Kim, Sung-Ho, and Namgil Lee. "A Bayes Shrinkage Estimation Method for Vector Autoregressive Models." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.769-065.

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MORAES, R. M., and L. S. MACHADO. "A FUZZY EXPONENTIAL NAIVE BAYES CLASSIFIER." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0035.

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Surawit, Apinya, Chukiat Viwatwongkasem, Pichitpong Soontornpipit, Prasong Kitidamrongsuk, Piangchan Rojanavipart, and Siam Sae-tang. "Empirical Bayes Hierarchical Modelling and Mapping of HIV/AIDS." In 2019 7th International Electrical Engineering Congress (iEECON). IEEE, 2019. http://dx.doi.org/10.1109/ieecon45304.2019.8938851.

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Priyanga, P., and N. C. Naveen. "Web Analytics Support System for Prediction of Heart Disease Using Naive Bayes Weighted Approach (NBwa)." In 2017 Asia Modelling Symposium (AMS). 11th International Conference on Mathematical Modelling & Computer Simulation. IEEE, 2017. http://dx.doi.org/10.1109/ams.2017.12.

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Aung, Swe Swe, and Thinn Thu Naing. "Naïve Bayes Classifier Based Traffic Prediction System on Cloud Infrastructure." In 2015 6th International Conference on Intelligent Systems, Modelling and Simulation (ISMS). IEEE, 2015. http://dx.doi.org/10.1109/isms.2015.45.

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"A Naive Bayes classifier for modeling distributions of the common reed in southern Finland." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.h3.altartouri.

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Fan, Jinxuan. "Predicting GRE score range using TOEFL score based on Bayes theorem." In 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), edited by Chi-Hua Chen, Xuexia Ye, and Hari Mohan Srivastava. SPIE, 2022. http://dx.doi.org/10.1117/12.2639353.

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Guo, Yuanyuan, and Xudong Zhang. "Radar Signal Classification Based on Cascade of STFT, PCA and Naïve Bayes." In 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS). IEEE, 2016. http://dx.doi.org/10.1109/isms.2016.48.

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Rajadhyaksha, Nishant. "Modelling Veracity of Football Player Trade Rumours on Twitter Using Naive Bayes Algorithm." In 2021 International Conference on Artificial Intelligence and Machine Vision (AIMV). IEEE, 2021. http://dx.doi.org/10.1109/aimv53313.2021.9670932.

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