A5020119606- Advanced Statistical Process Monitoring
- Species Distribution and Climate Change
- Coral and Marine Ecosystems Studies
- Anomaly Detection Techniques and Applications
- Data Stream Mining Techniques
- Advanced Statistical Methods and Models
- Statistical Methods and Bayesian Inference
- Sports Analytics and Performance
- Data Visualization and Analytics
- Time Series Analysis and Forecasting
- Gaussian Processes and Bayesian Inference
- Bayesian Methods and Mixture Models
- Hydrology and Watershed Management Studies
- Soil and Water Nutrient Dynamics
- Sleep and related disorders
- Infant Development and Preterm Care
- Fault Detection and Control Systems
- Bayesian Modeling and Causal Inference
- Sleep and Work-Related Fatigue
- Scientific Measurement and Uncertainty Evaluation
- Data Analysis with R
- Optimal Experimental Design Methods
- Network Security and Intrusion Detection
- Statistical and Computational Modeling
- Fish Ecology and Management Studies
Queensland University of Technology
2019-2024
Australian Research Council
2019-2022
ARC Centre of Excellence for Mathematical and Statistical Frontiers
2019-2022
Massey University
2014-2020
Australian Mathematical Sciences Institute
2020
Blücher (Germany)
2014
ANA Aeroportos de Portugal (Portugal)
2014
Universidad Central "Marta Abreu" de las Villas (UCLV)
2012
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part toolkit for most statisticians data scientists. Whether they dedicated Bayesians or opportunistic users, applied professionals can reap many benefits afforded by paradigm. In this paper, we touch six modern opportunities challenges in statistics: intelligent collection, new sources, federated analysis, inference implicit models, model...
Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach reporting from one 'best' model out several candidate models generally ignores uncertainty that arises selection, and in inferences are sensitive particular parameters chosen. Bayesian averaging (BMA) is a popular combining offers some attractive benefits this setting, including probabilistic interpretation combined...
Abstract The use of in‐situ digital sensors for water quality monitoring is becoming increasingly common worldwide. While these provide near real‐time data science, the are prone to technical anomalies that can undermine trustworthiness and accuracy statistical inferences, particularly in spatial temporal analyses. Here we propose a framework detecting sensor recorded stream networks, which takes advantage autocorrelation improve detection rates. proposed involves implementation effective...
Abstract Many research domains use data elicited from ‘citizen scientists’ when a direct measure of process is expensive or infeasible. However, participants may report incorrect estimates classifications due to their lack skill. We demonstrate how Bayesian hierarchical models can be used learn about latent variables interest, while accounting for the participants’ abilities. The model described in context an ecological application that involves crowdsourced georeferenced coral-reef images...
Abstract Citizen science projects have become increasingly popular in many fields, including ecology. However, the quality of this information is frequently debated within scientific community. Modern citizen implementations therefore require measures users' proficiency. We introduce a new methodological framework item response that quantifies scientist's ability, taking into account difficulty task. focus on programs involving classification images. Our approach accommodates spatial...
<ns3:p><ns3:bold>Background:</ns3:bold> Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems human societies. Real-time monitoring water increasingly reliant on in-situ sensor technology.</ns3:p><ns3:p> Anomaly detection for identifying erroneous patterns data, but can be challenging task due to complexity variability even under typical conditions. This paper presents solution anomaly network which essential accurate continuous...
Abstract Optimal design facilitates intelligent data collection. In this paper, we introduce a fully Bayesian approach for spatial processes with complex covariance structures, like those typically exhibited in natural ecosystems. Coordinate exchange algorithms are commonly used to find optimal points. However, collecting at specific points is often infeasible practice. Currently, there no provision allow flexibility the choice of design. Accordingly, also propose an sampling windows, rather...
Manufacturing processes are often based on more than one quality characteristic. When these variables correlated the process capability analysis should be performed using multivariate statistical methodologies. Although there is a growing interest in methods for evaluating of processes, little attention has been given to developing user friendly software supporting analysis. In this work we introduce package MPCI R, which allows compute indices. aims provide useful tool dealing with...
Following the introduction of high-resolution player tracking technology, a new range statistical analysis has emerged in sports, specifically basketball. However, such high-dimensional data are often challenging for inference and decision making. In this article we employ state-of-the-art Bayesian mixture model that allows estimation heterogeneous intrinsic dimension (ID) within dataset, propose some theoretical enhancements. Informally, ID can be seen as an indicator complexity dependence...
Summary Crowdsourcing methods facilitate the production of scientific information by non‐experts. This form citizen science (CS) is becoming a key source complementary data in many fields to inform data‐driven decisions and study challenging problems. However, concerns about validity these often constrain their utility. In this paper, we focus on use addressing complex challenges environmental conservation. We consider issue from three perspectives. First, present literature scan papers that...
So-called 'citizen science' data elicited from crowds has become increasingly popular in many fields including ecology. However, the quality of this information is being frequently debated by within scientific community. Therefore, modern citizen science implementations require measures users' proficiency that account for difficulty tasks. We introduce a new methodological framework item response and linear logistic test models with application to used ecology research. This approach...
Spatio-temporal models are widely used in many research areas from ecology to epidemiology. However, a limited number of computational tools available for modeling river network datasets space and time. In this paper, we introduce the `R` package [SSNbayes](https://CRAN.R-project.org/package=SSNbayes) fitting Bayesian spatio-temporal making predictions on branching stream networks. provides linear regression framework with multiple options incorporating spatial temporal autocorrelation....