Welldefined Decision Scenarios. - Thomas D. Nielsen; Finn Verner Jensen (UAI, 1999)

Representing and Solving Asymmetric Bayesian Decision Problems. - Thomas D. Nielsen; Finn Verner Jensen (UAI, 2000)

Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example. - Thomas D. Nielsen; Pierre-Henri Wuillemin; Finn Verner Jensen; Uffe Kjærulff (UAI, 2000)

Structural Learning in Object Oriented Domains. - Olav Bangsø; Helge Langseth; Thomas D. Nielsen (FLAIRS, 2001)

Cutting Influence Diagrams Down to the Core. - Thomas D. Nielsen; Finn Verner Jensen (SCAI, 2001)

Decomposition of influence diagrams. - Thomas D. Nielsen (J. Appl. Non Class. Logics, 2002)

Representing and Solving Asymmetric Decision Problems. - Thomas D. Nielsen; Finn Verner Jensen (Int. J. Inf. Technol. Decis. Mak., 2003)

Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. - Helge Langseth; Thomas D. Nielsen (J. Mach. Learn. Res., 2003)

Sensitivity analysis in influence diagrams. - Thomas D. Nielsen; Finn Verner Jensen (IEEE Trans. Syst. Man Cybern. Part A, 2003)

Learning a decision maker's utility function from (possibly) inconsistent behavior. - Thomas D. Nielsen; Finn Verner Jensen (Artif. Intell., 2004)

Latent variable discovery in classification models. - Nevin Lianwen Zhang; Thomas D. Nielsen; Finn Verner Jensen (Artif. Intell. Medicine, 2004)

Alert Systems for Production Plants: A Methodology Based on Conflict Analysis. - Thomas D. Nielsen; Finn Verner Jensen (ECSQARU, 2005)

Special Issue on ECSQARU-2003: The Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty: Message from the Guest Editors. - Thomas D. Nielsen; Nevin Lianwen Zhang (Int. J. Approx. Reason., 2005)

Latent Classification Models. - Helge Langseth; Thomas D. Nielsen (Mach. Learn., 2005)

A COTS framework for sensor fusion using dynamic bayesian networks in livestock production. - Jens A. Hansen; Thomas D. Nielsen; Henrik Schiøler (CAINE, 2006)

Sensor Fusion Using Dynamic Bayesian Networks in Livestock Production Buildings. - Jens A. Hansen; Thomas D. Nielsen; Henrik Schiøler (CIMCA/IAWTIC, 2006)

Dynamic decision making without expected utility: An operational approach. - Thomas D. Nielsen; Jean-Yves Jaffray (Eur. J. Oper. Res., 2006)

Sequential influence diagrams: A unified asymmetry framework. - Finn Verner Jensen; Thomas D. Nielsen; Prakash P. Shenoy (Int. J. Approx. Reason., 2006)

Classification using Hierarchical Naïve Bayes models. - Helge Langseth; Thomas D. Nielsen (Mach. Learn., 2006)

On-line alert systems for production plants: A conflict based approach. - Thomas D. Nielsen; Finn Verner Jensen (Int. J. Approx. Reason., 2007)

Adapting Bayes network structures to non-stationary domains. - Søren Holbech Nielsen; Thomas D. Nielsen (Int. J. Approx. Reason., 2008)

Maximum Likelihood Learning of Conditional MTE Distributions. - Helge Langseth; Thomas D. Nielsen; Rafael Rumí; Antonio Salmerón (ECSQARU, 2009)

A comparison of two approaches for solving unconstrained influence diagrams. - Kristian S. Ahlmann-Ohlsen; Finn Verner Jensen; Thomas D. Nielsen; Ole Pedersen; Marta Vomlelová (Int. J. Approx. Reason., 2009)

Latent classification models for binary data. - Helge Langseth; Thomas D. Nielsen (Pattern Recognit., 2009)

Inference in hybrid Bayesian networks. - Helge Langseth; Thomas D. Nielsen; Rafael Rumí; Antonio Salmerón (Reliab. Eng. Syst. Saf., 2009)

Towards a More Expressive Model for Dynamic Classification. - Shengtong Zhong; Ana M. Martínez; Thomas D. Nielsen; Helge Langseth (FLAIRS, 2010)

Special Issue on PGM-2008. - Manfred Jaeger; Thomas D. Nielsen (Int. J. Approx. Reason., 2010)

Parameter estimation and model selection for mixtures of truncated exponentials. - Helge Langseth; Thomas D. Nielsen; Rafael Rumí; Antonio Salmerón (Int. J. Approx. Reason., 2010)

Learning Probabilistic Automata for Model Checking. - Hua Mao; Yingke Chen; Manfred Jaeger; Thomas D. Nielsen; Kim G. Larsen; Brian Nielsen (QEST, 2011)

Probabilistic decision graphs for optimization under uncertainty. - Finn Verner Jensen; Thomas D. Nielsen (4OR, 2011)

Active Learning of Markov Decision Processes for System Verification. - Yingke Chen; Thomas Dyhre Nielsen (ICMLA (2), 2012)

Learning Markov Models for Stationary System Behaviors. - Yingke Chen; Hua Mao; Manfred Jaeger; Thomas Dyhre Nielsen; Kim Guldstrand Larsen; Brian Nielsen (NASA Formal Methods, 2012)

Learning Markov Decision Processes for Model Checking - Hua Mao; Yingke Chen; Manfred Jaeger; Thomas D. Nielsen; Kim G. Larsen; Brian Nielsen (QFM, 2012)

A latent model for collaborative filtering. - Helge Langseth; Thomas Dyhre Nielsen (Int. J. Approx. Reason., 2012)

Mixtures of truncated basis functions. - Helge Langseth; Thomas D. Nielsen; Rafael Rumí; Antonio Salmerón (Int. J. Approx. Reason., 2012)

Learning Mixtures of Polynomials of Conditional Densities from Data. - Pedro L. López-Cruz; Thomas D. Nielsen; Concha Bielza; Pedro Larrañaga (CAEPIA, 2013)

Requirement Engineering for a Small Project with Pre-Specified Scope. - Thomas D. Nielsen; Sigve Hovda; Antonio Fernández 002; Helge Langseth; Anders L. Madsen; Andrés R. Masegosa; Antonio Salmerón (NIK, 2014)

A Pairwise Class Interaction Framework for Multilabel Classification. - Jacinto Arias; José A. Gámez 001; Thomas D. Nielsen; José M. Puerta (Probabilistic Graphical Models, 2014)

A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. - Anders L. Madsen; Frank Jensen; Antonio Salmerón; Martin Karlsen; Helge Langseth; Thomas D. Nielsen (Probabilistic Graphical Models, 2014)

Special Issue on PGM-2012. - Andrés Cano; Manuel Gómez-Olmedo; Thomas Dyhre Nielsen (Int. J. Approx. Reason., 2014)

Learning mixtures of truncated basis functions from data. - Helge Langseth; Thomas D. Nielsen; Inmaculada Pérez-Bernabé; Antonio Salmerón (Int. J. Approx. Reason., 2014)

A classification-based approach to monitoring the safety of dynamic systems. - Shengtong Zhong; Helge Langseth; Thomas Dyhre Nielsen (Reliab. Eng. Syst. Saf., 2014)

Parallelisation of the PC Algorithm. - Anders L. Madsen; Frank Jensen; Antonio Salmerón; Helge Langseth; Thomas D. Nielsen (CAEPIA, 2015)

Parallel Importance Sampling in Conditional Linear Gaussian Networks. - Antonio Salmerón; Darío Ramos-López; Hanen Borchani; Ana M. Martínez; Andrés R. Masegosa; Antonio Fernández 002; Helge Langseth; Anders L. Madsen; Thomas D. Nielsen (CAEPIA, 2015)

MPE Inference in Conditional Linear Gaussian Networks. - Antonio Salmerón; Rafael Rumí; Helge Langseth; Anders L. Madsen; Thomas D. Nielsen (ECSQARU, 2015)

Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. - Hanen Borchani; Ana M. Martínez; Andrés R. Masegosa; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Antonio Fernández 002; Anders L. Madsen; Ramón Sáez (IDA, 2015)

Dynamic Bayesian modeling for risk prediction in credit operations. - Hanen Borchani; Ana M. Martínez; Andrés R. Masegosa; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Antonio Fernández 002; Anders L. Madsen; Ramón Sáez (SCAI, 2015)

Scalable learning of probabilistic latent models for collaborative filtering. - Helge Langseth; Thomas D. Nielsen (Decis. Support Syst., 2015)

Conditional Density Approximations with Mixtures of Polynomials. - Gherardo Varando; Pedro L. López-Cruz; Thomas D. Nielsen; Pedro Larrañaga; Concha Bielza (Int. J. Intell. Syst., 2015)

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. - Antonio Salmerón; Anders L. Madsen; Frank Jensen; Helge Langseth; Thomas D. Nielsen; Darío Ramos-López; Ana M. Martínez; Andrés R. Masegosa (ECAI, 2016)

Financial Data Analysis with PGMs Using AMIDST. - Rafael Cabañas 001; Ana M. Martínez; Andrés R. Masegosa; Darío Ramos-López; Antonio Salmerón; Thomas D. Nielsen; Helge Langseth; Anders L. Madsen (ICDM Workshops, 2016)

d-VMP: Distributed Variational Message Passing. - Andrés R. Masegosa; Ana M. Martínez; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Darío Ramos-López; Anders L. Madsen (Probabilistic Graphical Models, 2016)

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. - Darío Ramos-López; Antonio Salmerón; Rafael Rumí; Ana M. Martínez; Thomas D. Nielsen; Andrés R. Masegosa; Helge Langseth; Anders L. Madsen (Probabilistic Graphical Models, 2016)

A scalable pairwise class interaction framework for multidimensional classification. - Jacinto Arias; José A. Gámez 001; Thomas D. Nielsen; José M. Puerta (Int. J. Approx. Reason., 2016)

Anytime Decision Making Based on Unconstrained Influence Diagrams. - Manuel Luque; Thomas D. Nielsen; Finn Verner Jensen (Int. J. Intell. Syst., 2016)

Learning deterministic probabilistic automata from a model checking perspective. - Hua Mao; Yingke Chen; Manfred Jaeger; Thomas D. Nielsen; Kim G. Larsen; Brian Nielsen (Mach. Learn., 2016)

Bayesian Models of Data Streams with Hierarchical Power Priors. - Andrés R. Masegosa; Thomas D. Nielsen; Helge Langseth; Darío Ramos-López; Antonio Salmerón; Anders L. Madsen (ICML, 2017)

Scaling up Bayesian variational inference using distributed computing clusters. - Andrés R. Masegosa; Ana M. Martínez; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Darío Ramos-López; Anders L. Madsen (Int. J. Approx. Reason., 2017)

A parallel algorithm for Bayesian network structure learning from large data sets. - Anders L. Madsen; Frank Jensen; Antonio Salmerón; Helge Langseth; Thomas D. Nielsen (Knowl. Based Syst., 2017)

MAP inference in dynamic hybrid Bayesian networks. - Darío Ramos-López; Andrés R. Masegosa; Ana M. Martínez; Antonio Salmerón; Thomas D. Nielsen; Helge Langseth; Anders L. Madsen (Prog. Artif. Intell., 2017)

On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network. - Tobias Skovgaard Jepsen; Christian S. Jensen; Thomas Dyhre Nielsen; Kristian Torp (IEEE BigData, 2018)

Adaptive User-Oriented Direct Load-Control of Residential Flexible Devices. - Davide Frazzetto; Bijay Neupane; Torben Bach Pedersen; Thomas Dyhre Nielsen (e-Energy, 2018)

Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. - Darío Ramos-López; Andrés R. Masegosa; Antonio Salmerón; Rafael Rumí; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (Int. J. Approx. Reason., 2018)

A Review of Inference Algorithms for Hybrid Bayesian Networks. - Antonio Salmerón; Rafael Rumí; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (J. Artif. Intell. Res., 2018)

Graph Convolutional Networks for Road Networks. - Tobias Skovgaard Jepsen; Christian S. Jensen; Thomas Dyhre Nielsen (SIGSPATIAL/GIS, 2019)

On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network. - Tobias Skovgaard Jepsen; Christian S. Jensen; Thomas Dyhre Nielsen (CoRR, 2019)

AMIDST: A Java toolbox for scalable probabilistic machine learning. - Andrés R. Masegosa; Ana M. Martínez; Darío Ramos-López; Rafael Cabañas 001; Antonio Salmerón; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (Knowl. Based Syst., 2019)

Prescriptive analytics: a survey of emerging trends and technologies. - Davide Frazzetto; Thomas Dyhre Nielsen; Torben Bach Pedersen; Laurynas Siksnys (VLDB J., 2019)

Preface. - Thomas D. Nielsen; Manfred Jaeger (PGM, 2020)

Analyzing concept drift: A case study in the financial sector. - Andrés R. Masegosa; Ana M. Martínez; Darío Ramos-López; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón (Intell. Data Anal., 2020)

MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions. - Inmaculada Pérez-Bernabé; Ana D. Maldonado; Antonio Salmerón; Thomas D. Nielsen (R J., 2020)

Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds. - Martijn A. Goorden; Kim G. Larsen; Jesper E. Nielsen; Thomas D. Nielsen; Michael R. Rasmussen; Jirí Srba (ADHS, 2021)

SolveDB+: SQL-Based Prescriptive Analytics. - Laurynas Siksnys; Torben Bach Pedersen; Thomas Dyhre Nielsen; Davide Frazzetto (EDBT, 2021)

Probabilistic Models with Deep Neural Networks. - Andrés R. Masegosa; Rafael Cabañas 001; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón (Entropy, 2021)

Do You Feel the Same? On the Robustness of Cued-Recall Debriefing for User Experience Evaluation. - Anders Bruun; Effie Lai-Chong Law; Thomas Dyhre Nielsen; Matthias Heintz 001 (ACM Trans. Comput. Hum. Interact., 2021)

A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. - Antonio Salmerón; Helge Langseth; Andrés R. Masegosa; Thomas D. Nielsen (PGM, 2022)

Metagenomic binning with assembly graph embeddings. - Andre Lamurias; Mantas Sereika; Mads Albertsen; Katja Hose; Thomas Dyhre Nielsen (Bioinform., 2022)

Inducing Gaussian Process Networks. - Alessandro Tibo; Thomas Dyhre Nielsen (CoRR, 2022)

Graph Neural Networks for Microbial Genome Recovery. - Andre Lamurias; Alessandro Tibo; Katja Hose; Mads Albertsen; Thomas Dyhre Nielsen (CoRR, 2022)

Relational Fusion Networks: Graph Convolutional Networks for Road Networks. - Tobias Skovgaard Jepsen; Christian S. Jensen; Thomas Dyhre Nielsen (IEEE Trans. Intell. Transp. Syst., 2022)

UniTE - The Best of Both Worlds: Unifying Function-fitting and Aggregation-based Approaches to Travel Time and Travel Speed Estimation. - Tobias Skovgaard Jepsen; Christian S. Jensen; Thomas Dyhre Nielsen (ACM Trans. Spatial Algorithms Syst., 2022)

Patient Event Sequences for Predicting Hospitalization Length of Stay. - Emil Riis Hansen; Thomas Dyhre Nielsen; Thomas Mulvad; Mads Nibe Stausholm; Tomer Sagi; Katja Hose (AIME, 2023)

Guaranteed safe controller synthesis for switched systems using analytical solutions*. - Martijn A. Goorden; Kim G. Larsen; Jesper E. Nielsen; Thomas D. Nielsen; Weizhu Qian; Michael R. Rasmussen; Guohan Zhao (CCTA, 2023)

Metagenomic Binning using Connectivity-constrained Variational Autoencoders. - Andre Lamurias; Alessandro Tibo; Katja Hose; Mads Albertsen; Thomas Dyhre Nielsen (ICML, 2023)

Hospitalization Length of Stay Prediction using Patient Event Sequences. - Emil Riis Hansen; Thomas Dyhre Nielsen; Thomas Mulvad; Mads Nibe Stausholm; Tomer Sagi; Katja Hose (CoRR, 2023)

Energy Consumption Optimization in Radio Access Networks (ECO-RAN). - Anders Mariegaard; Kim G. Larsen; Marco Muñiz; Thomas Dyhre Nielsen (CoRR, 2023)

Compositional Control Synthesis for Water Management System. - Esther H. Kim; Kim G. Larsen; Martijn A. Goorden; Thomas D. Nielsen (ECC, 2024)

Integrating Multi-Modal Spatial Data using Knowledge Graphs - a Case Study of Microflora Danica. - Mads Corfixen; Thomas Heede; Tomer Sagi; Mads Albertsen; Thomas D. Nielsen; Katja Hose (SeWeBMeDA@ESWC, 2024)

Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning. - Abdulkadir Çelikkanat; Andrés R. Masegosa; Thomas D. Nielsen (CoRR, 2024)

Controlling stormwater detention ponds under partial observability. - Esther H. Kim; Martijn A. Goorden; Kim G. Larsen; Thomas D. Nielsen (J. Log. Algebraic Methods Program., 2024)

A study on the risk stratification for patients within 24 hours of admission for risk of hospital-acquired urinary tract infection using Bayesian network models. - Rune Sejer Jakobsen; Thomas Dyhre Nielsen; Peter Leutscher; Kristoffer Koch (Health Informatics J., 2024)

Uncertainty-Aware Temporal Graph Convolutional Network for Traffic Speed Forecasting. - Weizhu Qian; Thomas Dyhre Nielsen; Yan Zhao 008; Kim Guldstrand Larsen; James Jianqiao Yu (IEEE Trans. Intell. Transp. Syst., 2024)

Data-Driven Shielding of Online Reinforcement Learning: A Stormwater Pond Case Study. - Esther H. Kim; Martijn A. Goorden; Kim Guldstrand Larsen; Thomas Dyhre Nielsen (FSEN, 2025)