M. Troffaes

发表

Multinomial Logistic Regression on Markov Chains for Crop Rotation Modelling

Andy Hart, Matthias C. M. Troffaes, Mohamud Hussein, 2013, IPMU.

A Robust Bayesian Analysis of Variable Selection under Prior Ignorance

Matthias C. M. Troffaes, J. Einbeck, Tathagata Basu, 2022, Sankhya A.

A unifying approach to integration for bounded positive charges

Gert de Cooman, Matthias C. M. Troffaes, Enrique Miranda, 2008 .

Robustness of Natural Extension

R. Hable, M. Troffaes, 2011 .

Two-State Imprecise Markov Chains for Statistical Modelling of Two-State Non-Markovian Processes

Matthias C. M. Troffaes, Thomas E. Krak, Henna Bains, 2019, ISIPTA.

Imprecision in Statistical Theory and Practice

Pauline Coolen-Schrijner, Frank P. A. Coolen, Matthias C. M. Troffaes, 2009 .

Introduction to imprecise probabilities

Matthias C. M. Troffaes, Thomas Augustin, G. Cooman, 2014 .

Citation for Published Item: Use Policy Decision Making under Uncertainty Using Imprecise Probabilities

M. Troffaes, M. C. M. Troaes, 2022 .

Probability boxes on totally preordered spaces for multivariate modelling

Sébastien Destercke, Matthias C. M. Troffaes, Sébastien Destercke, 2011, Int. J. Approx. Reason..

Coherent lower previsions in systems modelling: products and aggregation rules

Gert de Cooman, Matthias C. M. Troffaes, G. Cooman, 2004, Reliab. Eng. Syst. Saf..

Deposited in DRO : 01 December 2014 Version of attached le : Accepted Version Peer-review status of attached

Matthias C. M. Troffaes, M. Troffaes, 2014 .

Dynamic Programming for Discrete-Time Systems with Uncertain Gain

Gert de Cooman, Matthias C. M. Troffaes, G. Cooman, 2003, ISIPTA.

Using Imprecise Continuous Time Markov Chains for Assessing the Reliability of Power Networks with Common Cause Failure and Non-Immediate Repair

Matthias C. M. Troffaes, Jacob Gledhill, D. Škulj, 2015 .

Sequential Decision Processes under Act-State Independence with Arbitrary Choice Functions

Matthias C. M. Troffaes, Ricardo Shirota Filho, Nathan Huntley, 2010, IPMU.

An Efficient Normal Form Solution to Decision Trees with Lower Previsions

Matthias C. M. Troffaes, Nathan Huntley, Nathan Huntley, 2008, SMPS.

Learning and Optimal Control of Imprecise Markov Decision Processes by Dynamic Programming Using the Imprecise Dirichlet Model

Matthias C. M. Troffaes, M. Troffaes, 2004 .

Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis

Matthias C. M. Troffaes, J. Lindström, U. Sahlin, 2022, Comput. Stat. Data Anal..

Contingency Ranking in Power Systems Via Reliability Rates

Matthias C. M. Troffaes, Behzad Kazemtabrizi, Carlos Jesús Ferrandon Cervantes, 2018, 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

A Review of Methods to Study Resilience of Complex Engineering and Engineered Systems

Edmondo Minisci, Weisi Guo, Matthias C. M. Troffaes, 2020, IEEE Access.

Decision Making with Imprecise Probabilities: A Short Review

Matthias C. M. Troffaes, C Troffaes, M. Troffaes, 2004 .

A robust Bayesian approach to modeling epistemic uncertainty in common-cause failure models

Matthias C. M. Troffaes, Dana Kelly, Gero Walter, 2013, Reliab. Eng. Syst. Saf..

Identifying subtree perfectness in decision trees

Matthias C. M. Troffaes, N. Huntley, Nathan Huntley, 2012, 1208.1154.

Dynamic programming for deterministic discrete-time systems with uncertain gain

Gert de Cooman, Matthias C. M. Troffaes, G. Cooman, 2005, Int. J. Approx. Reason..

Data analysis and robust modelling of the impact of renewable generation on long term security of supply and demand

Matthias C. M. Troffaes, Chris Dent, Edward Williams, 2015, 2015 IEEE Power & Energy Society General Meeting.

A robust Bayesian bias‐adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis

Matthias C. M. Troffaes, J. Lindström, U. Sahlin, 2022, Statistics in medicine.