no code implementations • 5 Jul 2024 • Manuele Leonelli, Jim Q. Smith, Sophia K. Wright
Bayesian networks are one of the most widely used classes of probabilistic models for risk management and decision support because of their interpretability and flexibility in including heterogeneous pieces of information.
1 code implementation • 22 Jan 2021 • Valerio Perrone, Simon Hengchen, Marco Palma, Alessandro Vatri, Jim Q. Smith, Barbara McGillivray
In this chapter we build on GASC, a recent computational approach to semantic change based on a dynamic Bayesian mixture model.
no code implementations • 8 Jul 2020 • Aditi Shenvi, F. Oliver Bunnin, Jim Q. Smith
Activities of terrorist groups present a serious threat to the security and well-being of the general public.
no code implementations • 29 Jun 2020 • Aditi Shenvi, Jim Q. Smith
This coloured event tree, also known as a staged tree, is the output of the learning algorithms used for this family.
no code implementations • 29 Jun 2020 • Aditi Shenvi, Jim Q. Smith
Chain Event Graphs (CEGs) are a family of event-based graphical models that represent context-specific conditional independences typically exhibited by asymmetric state space problems.
no code implementations • 14 Feb 2020 • Xuewen Yu, Jim Q. Smith, Linda Nichols
Causal theory is now widely developed with many applications to medicine and public health.
no code implementations • WS 2019 • Valerio Perrone, Marco Palma, Simon Hengchen, Alessandro Vatri, Jim Q. Smith, Barbara McGillivray
Word meaning changes over time, depending on linguistic and extra-linguistic factors.
no code implementations • 22 Oct 2018 • Rodrigo A. Collazo, Jim Q. Smith
An N Time-Slice DCEG (NT-DCEG) is a useful subclass of the DCEG class that exhibits a specific type of periodicity in its supporting tree graph and embodies a time-homogeneity assumption.
no code implementations • 17 Aug 2018 • Rodrigo A. Collazo, Jim Q. Smith
The Dynamic Chain Event Graph (DCEG) is able to depict many classes of discrete random processes exhibiting asymmetries in their developments and context-specific conditional probabilities structures.
no code implementations • 2 Aug 2016 • Manuele Leonelli, Jim Q. Smith
We then proceed with the construction of a directed expected utility network to support decision makers in the domain of household food security.
no code implementations • 28 Jul 2016 • Manuele Leonelli, Eva Riccomagno, Jim Q. Smith
For problems where all random variables and decision spaces are finite and discrete, here we develop a symbolic way to calculate the expected utilities of influence diagrams that does not require a full numerical representation.
no code implementations • 7 Dec 2015 • Manuele Leonelli, Christiane Görgen, Jim Q. Smith
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensively studied and implemented in different software packages.
no code implementations • 21 Jan 2015 • Peter A. Thwaites, Jim Q. Smith
Bayesian Networks (BNs) are popular graphical models for the representation of statistical problems embodying dependence relationships between a number of variables.
no code implementations • 4 Apr 2014 • Chris. J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens
This paper considers the problem of estimating the structure of multiple related directed acyclic graph (DAG) models.