100 citations · 207 across the 28 of their papers we have counts for
10 papers · 1 filter
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas +4
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are…
Comments on: "Hybrid Semiparametric Bayesian Networks"
Marco Scutari
Invited discussion on the paper "Hybrid Semiparametric Bayesian Networks" by David Atienza, Pedro Larranaga and Concha Bielza (TEST, 2022).
Bayesian Network Models for Incomplete and Dynamic Data
Marco Scutari
Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the compar…
Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms
Marco Scutari, Catharina Elisabeth Graafland, José Manuel Gutiérrez
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence test…
Using Genetic Distance to Infer the Accuracy of Genomic Prediction
Marco Scutari, Ian Mackay, David Balding
The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to…
Multiple Quantitative Trait Analysis Using Bayesian Networks
Marco Scutari, Phil Howell, David J. Balding +1
Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on mult…
Improving the Efficiency of Genomic Selection
Marco Scutari, Ian Mackay, David J. Balding
We investigate two approaches to increase the efficiency of phenotypic prediction from genome-wide markers, which is a key step for genomic selection (GS) in plant and animal breed…
Graphical Modelling in Genetics and Systems Biology
Marco Scutari
Graphical modelling has a long history in statistics as a tool for the analysis of multivariate data, starting from Wright's path analysis and Gibbs' applications to statistical ph…
Measures of Variability for Bayesian Network Graphical Structures
Marco Scutari
The structure of a Bayesian network includes a great deal of information about the probability distribution of the data, which is uniquely identified given some general distributio…
Structure Variability in Bayesian Networks
Marco Scutari
The structure of a Bayesian network encodes most of the information about the probability distribution of the data, which is uniquely identified given some general distributional a…