Belief functions and statistical inference

  1. T. Denoeux and S. Li. Frequency-Calibrated Belief Functions: Review and New Insights. International Journal of Approximate Reasoning, Vol. 92, Pages 232-254, 2018. pdf
  2. O. Kanjanatarakul, T. Denoeux and S. Sriboonchitta. Prediction of future observations using belief functions: a likelihood-based approach. International Journal of Approximate Reasoning, Vol. 72, pages 71-94, 2016. pdf
  3. T. Denoeux. Rejoinder on “Likelihood-based belief function: Justification and some extensions to low-quality data”. International Journal of Approximate Reasoning, Volume 55, Issue 7, pages 1614-1617, 2014. pdf
  4. T. Denoeux. Likelihood-based belief function: justification and some extensions to low-quality data. International Journal of Approximate Reasoning, Volume 55, Issue 7, pages 1535–1547, 2014. pdf
  5. O. Kanjanatarakul, S. Sriboonchitta and T. Denoeux. Forecasting using belief functions: an application to marketing econometrics. International Journal of Approximate Reasoning, Vol. 55, Issue 5, pages 1113–1128, 2014. pdf
  6. E. Ramasso and T. Denoeux. Making use of partial knowledge about hidden states in HMMs: an approach based on belief functions. IEEE Transactions on Fuzzy Systems, Vol. 22, Issue 2, pages 395-405, 2014. pdf
  7. N. Ben Abdallah, N. Mouhous-Voyneau and T. Denoeux. Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change. International Journal of Approximate Reasoning, Vol. 55, Issue 1, Part 3, pages 341–354, 2014. pdf
  8. T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, Vol. 25, Issue 1, pages 119-130, 2013. pdf
  9. A. Aregui and T. Denoeux. Constructing Consonant Belief Functions from Sample Data using Confidence Sets of Pignistic Probabilities. International Journal of Approximate Reasoning, vol. 49, Issue 3, pages 575–594, 2008. pdf
  10. T. Denoeux. Constructing Belief Functions from Sample Data Using Multinomial Confidence Regions. International Journal of Approximate Reasoning, Vol. 42, Issue 3, Pages 228-252, 2006. pdf