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Understanding the importance of landscape configuration on ecosystem service bundles at a high resolution in urban landscapes in the UK

James D. Karimi, Ron Corstanje, Jim A. Harris
2021 Landscape Ecology  
Methods Bayesian Belief Network models were used to test the influence of landscape configuration on ecosystem service interactions.  ...  This study used Bayesian Belief Networks to predict ecosystem service trade-offs and synergies in the urban area comprising the towns of Milton Keynes, Bedford and Luton, UK.  ...  as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.  ... 
doi:10.1007/s10980-021-01200-2 fatcat:l3t5kg2yxjhgdgylswioogy2re

Bayesian network modelling through qualitative patterns

Peter J.F. Lucas
2005 Artificial Intelligence  
In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions offered by the Bayesian-network formalism and the features of the problem  ...  Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs.  ...  In addition to influences, a qualitative probabilistic network includes synergies modelling interactions between influences.  ... 
doi:10.1016/j.artint.2004.10.011 fatcat:oi6yljawdncc5i6rfedrrfmzou

Introducing situational signs in qualitative probabilistic networks

Janneke H. Bolt, Linda C. van der Gaag, Silja Renooij
2005 International Journal of Approximate Reasoning  
We show how situational signs can be used upon inference and how they are updated as the state of the network changes.  ...  A qualitative probabilistic network is a graphical model of the probabilistic influences among a set of statistical variables, in which each influence is associated with a qualitative sign.  ...  Qualitative networks, however, model the probabilistic relationships between their variables at a higher abstraction level than Bayesian networks.  ... 
doi:10.1016/j.ijar.2004.05.009 fatcat:dhh47yjh4rcqxgejriytjjupcy

Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK

James D. Karimi, Jim A. Harris, Ron Corstanje
2021 Landscape Ecology  
Methods We used circuit theory to model urban bird flow of P. major and C. caeruleus at a 2 m spatial resolution in Bedford, Luton and Milton Keynes, UK, and Bayesian Belief Networks (BBNs) to assess the  ...  Objectives The objectives of this study were to use a Bayesian Belief Network approach to (1) assess whether functional connectivity drives ES trade-offs and synergies in urban areas and (2) assess the  ...  Bayesian Belief Networks Bayesian Belief Networks (BBNs) are multivariate statistical models, acknowledged for their unique probabilistic modelling approach and their high model transparency (Landuyt  ... 
doi:10.1007/s10980-021-01307-6 fatcat:ni5o3pblqrgxlpmmevlnrnzoue

Geographic Information Systems and Medical Geography: Toward a New Synergy

Daniel Z. Sui
2007 Geography Compass  
This article presents a comprehensive review of the reciprocal interaction between GIS and medical geography, and calls for a new synergy between both fields in future research and education efforts.  ...  A glimpse of the massive literature on GIS applications in medical geography quickly reveals three major distinctive research directions -database development, analysis and modeling, and mapping and visualization  ...  Watts and Strogatz (1998) identified the first small-world network model, which is believed to exist somewhere between a completely random network model and a completely regular network model, and has  ... 
doi:10.1111/j.1749-8198.2007.00027.x fatcat:q54s74takrh5xizanrqv5tyx5q

Advanced Algorithms of Bayesian Network Learning and Probabilistic Inference from Inconsistent Prior Knowledge and Sparse Data with Applications in Computational Biology and Computer Vision [chapter]

Rui Chang
2010 Bayesian Network  
knowledge in a domain to construct Bayesian networks and generate quantitative probability predictions from these models.  ...  Bayesian Network 54 evaluation in top-down methods). This score function is often the posterior probability function of a Bayesian network structure and parameters given the training data.  ...  It is important to distinguish between plain synergy and additive synergy since they represent distinct semantic scenarios in a domain.  ... 
doi:10.5772/46967 fatcat:ijic5ya535bzdhk6vgv4hunyia

Upgrading Ambiguous Signs in QPNs [article]

Janneke H. Bolt, Silja Renooij, Linda C. van der Gaag
2012 arXiv   pre-print
WA qualitative probabilistic network models the probabilistic relationships between its variables by means of signs. Non-monotonic influences have associated an ambiguous sign.  ...  We study the persistence and propagation of situational signs upon inference and give a method to establish the sign of a reduced influence.  ...  Figure 1 : 1 An example Bayesian network, modelling the influences of training (T) and fitness (F) on a feeling of well-being (W).  ... 
arXiv:1212.2445v1 fatcat:sgi47lr55radtph4mjsp5jmvnm

Hierarchical Qualitative Inference Model with Substructures [chapter]

Zehua Zhang, Duoqian Miao, Jin Qian
2011 Lecture Notes in Computer Science  
This paper presents a hierarchical qualitative inference model with substructures which to some extent can eliminate the qualitative impact of uncertainty and solve trade-off problems by metastructures  ...  Qualitative propagation influences in qualitative inferences are unlike and interrelated on the different hierarchy of knowledge granules, and quantitative information loss easily results in reasoning  ...  A qualitative probabilistic network also is described to a DAG model by variables and the probabilistic relationships between them, denoted G = (V (G), A(G)).  ... 
doi:10.1007/978-3-642-24425-4_94 fatcat:sst3jpqmazerroh2vpyzyruth4

Enhancing QPNs for Trade-off Resolution [article]

Silja Renooij, Linda C. van der Gaag
2013 arXiv   pre-print
An enhanced qualitative probabilistic network differs from a regular qualitative network in that it distinguishes between strong and weak influences.  ...  Qualitative probabilistic networks have been introduced as qualitative abstractions of Bayesian belief networks.  ...  Strongly positive product synergies are defi ned analo gously; zero product synergies and ambiguous product synergies again are defi ned as in regular qualitative networks.  ... 
arXiv:1301.6735v1 fatcat:qstfdye2zrbj7oxpda7b5s6cau

Machine learning approaches for drug combination therapies

Betül Güvenç Paltun, Samuel Kaski, Hiroshi Mamitsuka
2021 Briefings in Bioinformatics  
Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive.  ...  Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases.  ...  There are two main steps of the model: (1) drug functional network construction and (2) partitioning of the functional drug network into clusters using a Bayesian nonnegative matrix factorization.  ... 
doi:10.1093/bib/bbab293 pmid:34368832 pmcid:PMC8574999 fatcat:lam5d6lmonfinmqs32umlgzc3m

Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning

Jan Wildenhain, Michaela Spitzer, Sonam Dolma, Nick Jarvik, Rachel White, Marcia Roy, Emma Griffiths, David S. Bellows, Gerard D. Wright, Mike Tyers
2015 Cell Systems  
A model based on the chemical-genetic matrix and the global genetic interaction network failed to accurately predict synergism.  ...  Synergism between 8128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms.  ...  Giaever and L. Fischer for discussions, and M. Costanzo  ... 
doi:10.1016/j.cels.2015.12.003 pmid:27136353 pmcid:PMC5998823 fatcat:ugwykf7gwbdm7bgmh7khvyapou

Bayesian networks modelling in support to cross cutting analysis of water supply and sanitation in developing countries

C. Dondeynaz, J. López Puga, C. Carmona Moreno
2013 Hydrology and Earth System Sciences Discussions  
This paper suggests the use of Bayesian network modelling methods because they are more easily adapted to deal with non-normal distributions, and integrate a qualitative approach for data analysis.  ...  Probabilistic scenarios run from the models allow an assessment of the relationships between human development, external support, governance aspects, economic activities and water supply and sanitation  ...  This study benefited from the support of the RALCEA and ACE-Water projects (EC contracts AA 2010/241-167 and AA 2009/220-992, respectively) component: stakeholder mapping and participation.  ... 
doi:10.5194/hessd-10-2481-2013 fatcat:v25gjcbylfflrfx4hrgxp3vx4y

Bayesian networks modelling in support to cross-cutting analysis of water supply and sanitation in developing countries

C. Dondeynaz, J. López Puga, C. Carmona Moreno
2013 Hydrology and Earth System Sciences  
This paper suggests the use of Bayesian network modelling methods because they are more easily adapted to deal with non-normal distributions, and integrate a qualitative approach for data analysis.  ...  Probabilistic scenarios run from the models allow an assessment of the relationships between human development, external support, governance aspects, economic activities and water supply and sanitation  ...  This study benefited from the support of the RALCEA and ACE-Water projects (EC contracts AA 2010/241-167 and AA 2009/220-992, respectively) component: stakeholder mapping and participation.  ... 
doi:10.5194/hess-17-3397-2013 fatcat:zj5qp2txxrejrpurtaqwsi2qji

Synergistic Synthetic Biology: Units in Concert

Jean-Yves Trosset, Pablo Carbonell
2013 Frontiers in Bioengineering and Biotechnology  
The appropriate modeling, characterization, and design of synergies between biological parts and units will allow the discovery of yet unforeseeable, novel synthetic biology applications.  ...  To that end, synthetic constructs need to be adequately optimized through in silico modeling by choosing the right complement of genetic parts and by experimental tuning through directed evolution and  ...  ACKNOWLEDGMENTS Pablo Carbonell is supported by Genopole through an ATIGE Grant and by Agence Nationale de la Recherche. Authors would like to thank Dr M. Raman for proofreading the manuscript.  ... 
doi:10.3389/fbioe.2013.00011 pmid:25022769 pmcid:PMC4090895 fatcat:a3r2jrusxvg4fixmy6ra3cv2jq

The Nuclear Physics of Neutron Stars

J. Piekarewicz, R. Utama
2016 Acta Physica Polonica B  
In particular, we will discuss a novel method that combines modern theoretical approaches with Bayesian Neural Networks to build a new mass formula that is then used to compute the crustal composition.  ...  In this contribution, we will focus on the dynamics of neutron-rich matter with special emphasis on its impact on the structure and composition of the outer crust.  ...  This realization has created a powerful synergy between nuclear physics and astrophysics.  ... 
doi:10.5506/aphyspolb.47.659 fatcat:c4mexkcx2nabdi6fl5jdphp75u
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