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Found 12 papers, 6 papers with code
Dependent Cluster Mapping (DCMAP): Optimal clustering of directed acyclic graphs for statistical inference
A Directed Acyclic Graph (DAG) can be partitioned or mapped into clusters to support and make inference more computationally efficient in Bayesian Network (BN), Markov process and other models.
Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis
This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets.
Assessing the Spatial Structure of the Association between Attendance at Preschool and Childrens Developmental Vulnerabilities in Queensland Australia
The research explores the influence of preschool attendance (one year before full-time school) on the development of children during their first year of school.
Graph Neural Network-Based Anomaly Detection for River Network Systems
We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data.
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior.
Federated Variational Inference Methods for Structured Latent Variable Models
Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields.
A variational autoencoder-based nonnegative matrix factorisation model for deep dictionary learning
Construction of dictionaries using nonnegative matrix factorisation (NMF) has extensive applications in signal processing and machine learning.
clusterBMA: Bayesian model averaging for clustering
In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms.
On the intrinsic dimensionality of Covid-19 data: a global perspective
This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a country's stringency of lockdown policies.
Bayesian item response models for citizen science ecological data
We introduce a new methodological framework of item response and linear logistic test models with application to citizen science data used in ecology research.
Applications
A network flow approach to visualising the roles of covariates in random forests
Together these visualisations facilitate substantial insights into the roles of covariates in a random forest but do not communicate the frequencies of the hierarchies of covariates effects across the random forest or the orders in which covariates occur in these hierarchies.
Other Statistics
Computationally Efficient Simulation of Queues: The R Package queuecomputer
Approximate Bayesian computation could offer a straight-forward way to infer parameters for such networks if we could simulate data quickly enough.
Computation Optimization and Control
