3 citations · 4 across the 5 of their papers we have counts for
5 papers
Attribution of Predictive Uncertainties in Classification Models
Iker Perez, Piotr Skalski, Alec Barns-Graham +2
Predictive uncertainties in classification tasks are often a consequence of model inadequacy or insufficient training data. In popular applications, such as image processing, we ar…
Scalable Bayesian Inference for Population Markov Jump Processes
Iker Perez, Theodore Kypraios
Bayesian inference for Markov jump processes (MJPs) where available observations relate to either system states or jumps typically relies on data-augmentation Markov Chain Monte Ca…
Variational inequalities and mean-field approximations for partially observed systems of queueing networks
Iker Perez, Giuliano Casale
Queueing networks are systems of theoretical interest that find widespread use in the performance evaluation of interconnected resources. In comparison to counterpart models in gen…
On Bayesian inferential tasks with infinite-state jump processes: efficient data augmentation
Iker Perez, Lax Chan, Mercedes Torres Torres +2
Advances in sampling schemes for Markov jump processes have recently enabled multiple inferential tasks. However, in statistical and machine learning applications, we often require…
Auxiliary Variables for Bayesian Inference in Multi-Class Queueing Networks
Iker Perez, David Hodge, Theodore Kypraios
Queue networks describe complex stochastic systems of both theoretical and practical interest. They provide the means to assess alterations, diagnose poor performance and evaluate…