Learning Mixtures of Polynomials of Conditional Densities from Data

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique for hybrid Bayesian networks with continuous and discrete variables. We propose two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate the methods using data sampled from a simple Gaussian Bayesian network. We study and compare the performance of these methods with the approach for learning mixtures of truncated basis functions from data

Approximate modelling of the multi-dimensional learner

This paper describes the design of the learner modelling component of the LeActiveMath system, which was conceived to integrate modelling of learners' competencies in a subject domain, motivational and affective dispositions and meta-cognition. This goal has been achieved by organising learner models as stacks, with the subject domain as ground layer and competency, motivation, affect and meta-cognition as upper layers. A concept map per layer defines each layer's elements and internal structure, and beliefs are associated to the applications of elements in upper-layers to elements in lower-layers. Beliefs are represented using belief functions and organised in a network constructed as the composition of all layers' concept maps, which is used for propagation of evidence

COVAD survey 2 long-term outcomes: unmet need and protocol

Vaccine hesitancy is considered a major barrier to achieving herd immunity against COVID-19. While multiple alternative and synergistic approaches including heterologous vaccination, booster doses, and antiviral drugs have been developed, equitable vaccine uptake remains the foremost strategy to manage pandemic. Although none of the currently approved vaccines are live-attenuated, several reports of disease flares, waning protection, and acute-onset syndromes have emerged as short-term adverse events after vaccination. Hence, scientific literature falls short when discussing potential long-term effects in vulnerable cohorts. The COVAD-2 survey follows on from the baseline COVAD-1 survey with the aim to collect patient-reported data on the long-term safety and tolerability of COVID-19 vaccines in immune modulation. The e-survey has been extensively pilot-tested and validated with translations into multiple languages. Anticipated results will help improve vaccination efforts and reduce the imminent risks of COVID-19 infection, especially in understudied vulnerable groups

Directed Evidential Networks with Conditional Belief Functions

The main question addressed in this paper is how to represent belief functions independencies by graphical model. Directed evidential networks (DEVNs) with conditional belief functions are then proposed. These networks are directed acyclic graphs (DAGs) similar to Bayesian networks but instead of using probability functions, we use belief functions. Directed evidential network with conditional belief functions has the advantage of providing an appropriate representation of the knowledge that can be produced as conditional relationships.SCOPUS: cp.pinfo:eu-repo/semantics/publishe