This book features 29 peer-reviewed papers presented at the 9th International Conference on
Soft Methods in Probability and Statistics (SMPS 2018) which was held in conjunction with the
5th International Conference on Belief Functions (BELIEF 2018) in Compiègne France on
September 17-21 2018. It includes foundational methodological and applied contributions on
topics as varied as imprecise data handling linguistic summaries model coherence imprecise
Markov chains and robust optimisation. These proceedings were produced using EasyChair.Over
recent decades interest in extensions and alternatives to probability and statistics has
increased significantly in diverse areas including decision-making data mining and machine
learning and optimisation. This interest stems from the need to enrich existing models in
order to include different facets of uncertainty like ignorance vagueness randomness
conflict or imprecision. Frameworks such as rough sets fuzzy sets fuzzy random variables
random sets belief functions possibility theory imprecise probabilities lower previsions
and desirable gambles all share this goal but have emerged from different needs. The advances
results and tools presented in this book are important in the ubiquitous and fast-growing
fields of data science machine learning and artificial intelligence. Indeed an important
aspect of some of the learned predictive models is the trust placed in them. Modelling the
uncertainty associated with the data and the models carefully and with principled methods is
one of the means of increasing this trust as the model will then be able to distinguish
between reliable and less reliable predictions. In addition extensions such as fuzzy sets can
be explicitly designed to provide interpretable predictive models facilitating user
interaction and increasing trust.
