Thomas D. Nielsen

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 35

Top Topics

Linear Gaussian

Basis Functions

Conditional Density

Bayesian Models

Top Venues

Int. J. Approx. Reason.

Probabilistic Graphical Models

Publications

Mads Corfixen, Thomas Heede, Tomer Sagi, Mads Albertsen, Thomas D. Nielsen, Katja Hose
Integrating Multi-Modal Spatial Data using Knowledge Graphs - a Case Study of Microflora Danica. SeWeBMeDA@ESWC (2024)
Esther H. Kim, Kim G. Larsen, Martijn A. Goorden, Thomas D. Nielsen
Compositional Control Synthesis for Water Management System. ECC (2024)
Esther H. Kim, Martijn A. Goorden, Kim G. Larsen, Thomas D. Nielsen
Controlling stormwater detention ponds under partial observability. J. Log. Algebraic Methods Program. 141 (2024)
Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Weizhu Qian, Michael R. Rasmussen, Guohan Zhao
. CCTA (2023)
Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen
A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. PGM (2022)
Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
Probabilistic Models with Deep Neural Networks. Entropy 23 (1) (2021)
Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Michael R. Rasmussen, Jirí Srba
Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds. ADHS (2021)
Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Michael R. Rasmussen, Jirí Srba
Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds. CoRR (2021)
Inmaculada Pérez-Bernabé, Ana D. Maldonado, Antonio Salmerón, Thomas D. Nielsen
MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions. R J. 12 (2) (2020)
Thomas D. Nielsen, Manfred Jaeger
Preface. PGM (2020)
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. 24 (3) (2020)
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. 163 (2019)
Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
Probabilistic Models with Deep Neural Networks. CoRR (2019)
Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
A Review of Inference Algorithms for Hybrid Bayesian Networks. J. Artif. Intell. Res. 62 (2018)
Darío Ramos-López, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. Int. J. Approx. Reason. 100 (2018)
Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
Bayesian Models of Data Streams with Hierarchical Power Priors. ICML (2017)
Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen
A parallel algorithm for Bayesian network structure learning from large data sets. Knowl. Based Syst. 117 (2017)
Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. 6 (2) (2017)
Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. 88 (2017)
Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
Bayesian Models of Data Streams with Hierarchical Power Priors. CoRR (2017)
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR (2017)
Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models (2016)
Manuel Luque, Thomas D. Nielsen, Finn Verner Jensen
Anytime Decision Making Based on Unconstrained Influence Diagrams. Int. J. Intell. Syst. 31 (4) (2016)
Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI (2016)
Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models (2016)
Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops (2016)
Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen
Learning deterministic probabilistic automata from a model checking perspective. Mach. Learn. 105 (2) (2016)
Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta
A scalable pairwise class interaction framework for multidimensional classification. Int. J. Approx. Reason. 68 (2016)
Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA (2015)
Helge Langseth, Thomas D. Nielsen
Scalable learning of probabilistic latent models for collaborative filtering. Decis. Support Syst. 74 (2015)
Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen
Parallelisation of the PC Algorithm. CAEPIA (2015)
Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen
Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA (2015)
Antonio Salmerón, Rafael Rumí, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen
MPE Inference in Conditional Linear Gaussian Networks. ECSQARU (2015)
Gherardo Varando, Pedro L. López-Cruz, Thomas D. Nielsen, Pedro Larrañaga, Concha Bielza
Conditional Density Approximations with Mixtures of Polynomials. Int. J. Intell. Syst. 30 (3) (2015)
Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
Dynamic Bayesian modeling for risk prediction in credit operations. SCAI (2015)
Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón
Requirement Engineering for a Small Project with Pre-Specified Scope. NIK (2014)
Anders L. Madsen, Frank Jensen, Antonio Salmerón, Martin Karlsen, Helge Langseth, Thomas D. Nielsen
A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. Probabilistic Graphical Models (2014)
Helge Langseth, Thomas D. Nielsen, Inmaculada Pérez-Bernabé, Antonio Salmerón
Learning mixtures of truncated basis functions from data. Int. J. Approx. Reason. 55 (4) (2014)
Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta
A Pairwise Class Interaction Framework for Multilabel Classification. Probabilistic Graphical Models (2014)
Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe Kjærulff
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example CoRR (2013)
Thomas D. Nielsen, Finn Verner Jensen
Representing and Solving Asymmetric Bayesian Decision Problems CoRR (2013)
Thomas D. Nielsen, Finn Verner Jensen
Welldefined Decision Scenarios CoRR (2013)
Pedro L. López-Cruz, Thomas D. Nielsen, Concha Bielza, Pedro Larrañaga
Learning Mixtures of Polynomials of Conditional Densities from Data. CAEPIA (2013)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
Mixtures of truncated basis functions. Int. J. Approx. Reason. 53 (2) (2012)
Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen
Learning Markov Decision Processes for Model Checking QFM (2012)
Finn Verner Jensen, Thomas D. Nielsen
Probabilistic decision graphs for optimization under uncertainty. 4OR 9 (1) (2011)
Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen
Learning Probabilistic Automata for Model Checking. QEST (2011)
Manfred Jaeger, Thomas D. Nielsen
Special Issue on PGM-2008. Int. J. Approx. Reason. 51 (5) (2010)
Shengtong Zhong, Ana M. Martínez, Thomas D. Nielsen, Helge Langseth
Towards a More Expressive Model for Dynamic Classification. FLAIRS Conference (2010)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
Parameter estimation and model selection for mixtures of truncated exponentials. Int. J. Approx. Reason. 51 (5) (2010)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
Maximum Likelihood Learning of Conditional MTE Distributions. ECSQARU (2009)
Kristian S. Ahlmann-Ohlsen, Finn Verner Jensen, Thomas D. Nielsen, Ole Pedersen, Marta Vomlelová
A comparison of two approaches for solving unconstrained influence diagrams. Int. J. Approx. Reason. 50 (1) (2009)
Helge Langseth, Thomas D. Nielsen
Latent classification models for binary data. Pattern Recognit. 42 (11) (2009)
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
Inference in hybrid Bayesian networks. Reliab. Eng. Syst. Saf. 94 (10) (2009)
Søren Holbech Nielsen, Thomas D. Nielsen
Adapting Bayes network structures to non-stationary domains. Int. J. Approx. Reason. 49 (2) (2008)
Thomas D. Nielsen, Finn Verner Jensen
On-line alert systems for production plants: A conflict based approach. Int. J. Approx. Reason. 45 (2) (2007)
Thomas D. Nielsen, Jean-Yves Jaffray
Dynamic decision making without expected utility: An operational approach. Eur. J. Oper. Res. 169 (1) (2006)
Jens A. Hansen, Thomas D. Nielsen, Henrik Schiøler
Sensor Fusion Using Dynamic Bayesian Networks in Livestock Production Buildings. CIMCA/IAWTIC (2006)
Søren Holbech Nielsen, Thomas D. Nielsen
Adapting Bayes Network Structures to Non-stationary Domains. Probabilistic Graphical Models (2006)
Jens A. Hansen, Thomas D. Nielsen, Henrik Schiøler
A COTS framework for sensor fusion using dynamic bayesian networks in livestock production. CAINE (2006)
Finn Verner Jensen, Thomas D. Nielsen, Prakash P. Shenoy
Sequential influence diagrams: A unified asymmetry framework. Int. J. Approx. Reason. 42 (1-2) (2006)
Helge Langseth, Thomas D. Nielsen
Classification using Hierarchical Naïve Bayes models. Mach. Learn. 63 (2) (2006)
Thomas D. Nielsen, Nevin Lianwen Zhang
Special Issue on ECSQARU-2003: The Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty: Message from the Guest Editors. Int. J. Approx. Reason. 38 (3) (2005)
Thomas D. Nielsen, Finn Verner Jensen
Alert Systems for Production Plants: A Methodology Based on Conflict Analysis. ECSQARU (2005)
Helge Langseth, Thomas D. Nielsen
Latent Classification Models. Mach. Learn. 59 (3) (2005)
Thomas D. Nielsen, Finn Verner Jensen
Learning a decision maker's utility function from (possibly) inconsistent behavior. Artif. Intell. 160 (1-2) (2004)
Nevin Lianwen Zhang, Thomas D. Nielsen, Finn Verner Jensen
Latent variable discovery in classification models. Artif. Intell. Medicine 30 (3) (2004)
Thomas D. Nielsen, Finn Verner Jensen
Sensitivity analysis in influence diagrams. IEEE Trans. Syst. Man Cybern. Part A 33 (2) (2003)
Helge Langseth, Thomas D. Nielsen
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. J. Mach. Learn. Res. 4 (2003)
Thomas D. Nielsen, Finn Verner Jensen
Representing and Solving Asymmetric Decision Problems. Int. J. Inf. Technol. Decis. Mak. 2 (2) (2003)
Thomas D. Nielsen
Decomposition of influence diagrams. J. Appl. Non Class. Logics 12 (2) (2002)
Olav Bangsø, Helge Langseth, Thomas D. Nielsen
Structural Learning in Object Oriented Domains. FLAIRS Conference (2001)
Thomas D. Nielsen, Finn Verner Jensen
Cutting Influence Diagrams Down to the Core. SCAI (2001)
Thomas D. Nielsen
Decomposition of Influence Diagrams. ECSQARU (2001)
Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe Kjærulff
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example. UAI (2000)
Thomas D. Nielsen, Finn Verner Jensen
Representing and Solving Asymmetric Bayesian Decision Problems. UAI (2000)
Thomas D. Nielsen, Finn Verner Jensen
Welldefined Decision Scenarios. UAI (1999)