ORCID Profile
0000-0001-8943-067X
Current Organisation
University of New South Wales
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Statistics | Applied Statistics | Statistical Theory | Applied Statistics | Statistical Theory | Operations Research | Statistical Mechanics, Physical Combinatorics and Mathematical Aspects of Condensed Matter | Stochastic Analysis and Modelling | Climatology (Incl. Palaeoclimatology) |
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in the Mathematical Sciences | Mathematical sciences | Climate variability | Climate Variability (excl. Social Impacts) | Expanding Knowledge in the Environmental Sciences | Other environmental aspects | Biological sciences | Superannuation and Insurance Services | Expanding Knowledge in the Medical and Health Sciences
Related Links
Publications
A spectral and Bayesian approach for analysis of fluctuations and synchrony in ecological datasets
Publisher: Wiley
Date: 21-09-2012
Hawkes Processes With Stochastic Exogenous Effects for Continuous-Time Interaction Modelling
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Quantifying the dependence between extreme rainfall and storm surge in the coastal zone
Publisher: Elsevier BV
Date: 11-2013
A distance-based diagnostic for trans-dimensional Markov chains
Publisher: Springer Science and Business Media LLC
Date: 08-2007
Ensemble of ARIMA: combining parametric and bootstrapping technique for traffic flow prediction
Publisher: Informa UK Limited
Date: 2020
A note on bayesian analyses of capture-recapture data with perfect recaptures
Publisher: Informa UK Limited
Date: 02-2006
Estimating animal movement contacts between holdings of different production types
Publisher: Elsevier BV
Date: 06-2010
DOI: 10.1016/J.PREVETMED.2010.03.002
Abstract: Animal movement poses a great risk for disease transmission between holdings. Heterogeneous contact patterns are known to influence the dynamics of disease transmission and should be included in modeling. Using pig movement data from Sweden as an ex le, we present a method for quantification of between holding contact probabilities based on different production types. The data contained seven production types: Sow pool center, Sow pool satellite, Farrow-to-finish, Nucleus herd, Piglet producer, Multiplying herd and Fattening herd. The method also estimates how much different production types will determine the contact pattern of holdings that have more than one type. The method is based on Bayesian analysis and uses data from central databases of animal movement. Holdings with different production types are estimated to vary in the frequency of contacts as well as in what type of holding they have contact with, and the direction of the contacts. Movements from Multiplying herds to Sow pool centers, Nucleus herds to other Nucleus herds, Sow pool centers to Sow pool satellites, Sow pool satellites to Sow pool centers and Nucleus herds to Multiplying herds were estimated to be most common relative to the abundance of the production types. We show with a simulation study that these contact patterns may also be expected to result in substantial differences in disease transmission via animal movements, depending on the index holding. Simulating transmission for a 1 year period showed that the median number of infected holdings was 1 (i.e. only the index holding infected) if the infection started at a Fattening herd and 2161 if the infection started on a Nucleus herd. We conclude that it is valuable to include production types in models of disease transmission and the method presented in this paper may be used for such models when appropriate data is available. We also argue that keeping records of production types is of great value since it may be helpful in risk assessments.
Composite likelihood methods for histogram-valued random variables
Publisher: Springer Science and Business Media LLC
Date: 11-06-2020
Using approximate bayesian computation to estimate tuberculosis transmission parameters from genotype data
Publisher: Oxford University Press (OUP)
Date: 07-2006
DOI: 10.1534/GENETICS.106.055574
Abstract: Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the net transmission rate, the doubling time, and the reproductive value of the pathogen. This method is applied to a published data set from San Francisco of tuberculosis genotypes based on the marker IS6110. The mutation rate of this marker has previously been studied, and we use those estimates to form a prior distribution of mutation rates in the inference procedure. The posterior point estimates of the key parameters of interest for these data are as follows: net transmission rate, 0.69/year [95% credibility interval (C.I.) 0.38, 1.08] doubling time, 1.08 years (95% C.I. 0.64, 1.82) and reproductive value 3.4 (95% C.I. 1.4, 79.7). These figures suggest a rapidly spreading epidemic, consistent with observations of the resurgence of tuberculosis in the United States in the 1980s and 1990s.
Likelihood-Based Inference for Modelling Packet Transit From Thinned Flow Summaries
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Adaptive optimal scaling of Metropolis–Hastings algorithms using the Robbins–Monro process
Publisher: Informa UK Limited
Date: 05-07-2016
The sensitivity of daily temperature variability and extremes to dataset choice
Publisher: American Meteorological Society
Date: 06-01-2023
Abstract: Robust conclusions regarding changes in the temperature distribution rely on the accuracy and reliability of the input datasets used. Differences between methodologies and datasets in previous studies add uncertainty when comparing and quantifying findings. Here, the authors investigate the sensitivity of assessing global and regional temperature variability and extremes over 1980–2014 in gridded datasets of daily temperature anomalies. A gridded in situ–based dataset, Hadley Centre Global Historical Climatology Network–Daily (HadGHCND), is compared against several commonly used reanalysis products by assessing both the entire distribution and the tails of the distribution. Empirical probability distribution functions show sensitivity to the input dataset when estimating aspects such as standard deviation and skewness, with the mean showing robust results for most regions, irrespective of dataset choice. Standard deviation is especially sensitive, with larger disagreements between datasets for some regions more than others, such as Africa and the Mediterranean region, and with larger differences in minimum temperatures compared with maximum temperatures. Estimates of extreme parameters also show sensitivity to dataset choice, particularly in the lower tails and for daily minimum temperature anomalies. Comparing changes in the means and the extremes of the temperature distributions, the cold extremes in the lower tails have been warming at a faster rate than the mean of the entire distribution for much of the Northern Hemisphere extratropics, with warm extremes warming at a faster rate than the mean in some subtropical regions. These documented sensitivities call for caution when assessing changes in temperature variability and extremes, as dataset choice can have substantial effects on results.
Does Amazonian deforestation cause global effects; Can we be sure?
Publisher: American Geophysical Union (AGU)
Date: 27-05-2016
DOI: 10.1002/2015JD024357
Transdimensional Markov chains: A decade of progress and future perspectives
Publisher: Informa UK Limited
Date: 09-2005
Diagnostic tools for approximate Bayesian computation using the coverage property
Publisher: Wiley
Date: 11-05-2202
DOI: 10.1111/ANZS.12087
Patterns and comparisons of human-induced changes in river flood impacts in cities
Publisher: Copernicus GmbH
Date: 13-03-2018
DOI: 10.5194/HESS-22-1793-2018
Abstract: Abstract. In this study, information extracted from the first global urban fluvial flood risk data set (Aqueduct) is investigated and visualized to explore current and projected city-level flood impacts driven by urbanization and climate change. We use a novel adaption of the self-organizing map (SOM) method, an artificial neural network proficient at clustering, pattern extraction, and visualization of large, multi-dimensional data sets. Prevalent patterns of current relationships and anticipated changes over time in the nonlinearly-related environmental and social variables are presented, relating urban river flood impacts to socioeconomic development and changing hydrologic conditions. Comparisons are provided between 98 in idual cities. Output visualizations compare baseline and changing trends of city-specific exposures of population and property to river flooding, revealing relationships between the cities based on their relative map placements. Cities experiencing high (or low) baseline flood impacts on population and/or property that are expected to improve (or worsen), as a result of anticipated climate change and development, are identified and compared. This paper condenses and conveys large amounts of information through visual communication to accelerate the understanding of relationships between local urban conditions and global processes.
Likelihood-free Bayesian inference for α-stable models
Publisher: Elsevier BV
Date: 11-2012
A model-based Bayesian estimation of the rate of evolution of VNTR loci in Mycobacterium tuberculosis
Publisher: Public Library of Science (PLoS)
Date: 28-06-0030
Evaluating extreme risks in invasion ecology: Learning from banking compliance
Publisher: Wiley
Date: 31-03-2008
Bayesian symbol detection in wireless relay networks via likelihood-free inference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2010
Bayesian analysis of animal movements related to factors at herd and between herd levels: Implications for disease spread modeling
Publisher: Elsevier BV
Date: 03-2011
DOI: 10.1016/J.PREVETMED.2010.11.005
Abstract: A method to assess the influence of between herd distances, production types and herd sizes on patterns of between herd contacts is presented. It was applied on pig movement data from a central database of the Swedish Board of Agriculture. To determine the influence of these factors on the contact between holdings we used a Bayesian model and Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model parameters. The analysis showed that the contact pattern via animal movements is highly heterogeneous and influenced by all three factors, production type, herd size, and distance between holdings. Most production types showed a positive relationship between maximum capacity and the probability of both incoming and outgoing movements. In agreement with previous studies, holdings also differed in both the number of contacts as well as with what holding types contact occurred with. Also, the scale and shape of distance dependence in contact probability was shown to differ depending on the production types of holdings.To demonstrate how the methodology may be used for risk assessment, disease transmissions via animal movements were simulated with the model used for analysis of contacts, and parameterized by the analyzed posterior distribution. A Generalized Linear Model showed that herds with production types Sow pool center, Multiplying herd and Nucleus herd have higher risk of generating a large number of new infections. Multiplying herds are also expected to generate many long distance transmissions, while transmissions generated by Sow pool centers are confined to more local areas. We argue that the methodology presented may be a useful tool for improvement of risk assessment based on data found in central databases.
Models for Extremal Dependence Derived from Skew-symmetric Families
Publisher: Wiley
Date: 13-09-2017
DOI: 10.1111/SJOS.12240
Modelling pathogen log10 reduction values achieved by activated sludge treatment using naïve and semi naïve Bayes network models
Publisher: Elsevier BV
Date: 11-2015
DOI: 10.1016/J.WATRES.2015.08.035
Abstract: Risk management for wastewater treatment and reuse have led to growing interest in understanding and optimising pathogen reduction during biological treatment processes. However, modelling pathogen reduction is often limited by poor characterization of the relationships between variables and incomplete knowledge of removal mechanisms. The aim of this paper was to assess the applicability of Bayesian belief network models to represent associations between pathogen reduction, and operating conditions and monitoring parameters and predict AS performance. Naïve Bayes and semi-naïve Bayes networks were constructed from an activated sludge dataset including operating and monitoring parameters, and removal efficiencies for two pathogens (native Giardia lamblia and seeded Cryptosporidium parvum) and five native microbial indicators (F-RNA bacteriophage, Clostridium perfringens, Escherichia coli, coliforms and enterococci). First we defined the Bayesian network structures for the two pathogen log10 reduction values (LRVs) class nodes discretized into two states (< and ≥ 1 LRV) using two different learning algorithms. Eight metrics, such as Prediction Accuracy (PA) and Area Under the receiver operating Curve (AUC), provided a comparison of model prediction performance, certainty and goodness of fit. This comparison was used to select the optimum models. The optimum Tree Augmented naïve models predicted removal efficiency with high AUC when all system parameters were used simultaneously (AUCs for C. parvum and G. lamblia LRVs of 0.95 and 0.87 respectively). However, metrics for in idual system parameters showed only the C. parvum model was reliable. By contrast in idual parameters for G. lamblia LRV prediction typically obtained low AUC scores (AUC < 0.81). Useful predictors for C. parvum LRV included solids retention time, turbidity and total coliform LRV. The methodology developed appears applicable for predicting pathogen removal efficiency in water treatment systems generally.
Towards automating model selection for a mark-recapture-recovery analysis
Publisher: Oxford University Press (OUP)
Date: 10-02-2009
DOI: 10.1111/J.1467-9876.2008.00656.X
Abstract: Methods for fitting models to mark–recapture–recovery studies are now well established in the literature. Classical model selection methods for identifying those models which best represent the population under investigation are perhaps less satisfactory. One class of methods implements manual model searches on a model space that is restricted by strong physical understandings of the biological plausibility of each model. This can lead to highly subjective analyses requiring a priori expert knowledge, which are slow to implement and can be error prone. More automated search algorithms are now available and can be implemented with ease to consider larger classes of models. We investigate the utility of such automated algorithms and consider in particular the situation where there is a large set of near optimal models according to the model ranking function. We present a modification of an automated search procedure on an unrestricted model space and propose a procedure for model selection in the absence of a single clear optimal model. We investigate this approach through a classical mark–recapture–recovery analysis of a red deer population from the island of Rùm and conduct an investigation into senesence, which is theorized to occur in wild animal populations.
Sequential Monte Carlo without likelihoods
Publisher: Proceedings of the National Academy of Sciences
Date: 06-02-2007
Abstract: Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection s ling or Markov chain Monte Carlo can be highly inefficient and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo s ler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.
Increasing dependence on foreign water resources? An assessment of trends in global virtual water flows using a self-organizing time map
Publisher: Elsevier BV
Date: 03-2015
Smaller projected increases in 20-year temperature returns over Australia in skill-selected climate models
Publisher: American Geophysical Union (AGU)
Date: 03-2009
DOI: 10.1029/2009GL037293
A dimension range representation (DRR) measure for self-organizing maps
Publisher: Elsevier BV
Date: 05-2016
Sequential Monte Carlo without likelihoods (Proceedings of the National Academy of Sciences of the United States of America (2007) 104, 6, (1760-1765) DOI:10.1073/pnas.0607208104)
Publisher: Proceedings of the National Academy of Sciences
Date: 29-09-2009
Bayesian Point Estimation of Quantitative Trait Loci
Publisher: Wiley
Date: 03-2004
Trends in cocaine use, markets and harms in Australia, 2003–2019
Publisher: Wiley
Date: 24-02-2021
DOI: 10.1111/DAR.13252
Abstract: This paper aims to describe cocaine use, markets and harms in Australia from 2003 to 2019. Outcome indicators comprised prevalence of use from triennial household surveys patterns of use from annual surveys of sentinel s les who use stimulants and cocaine‐related seizures, arrests, hospitalisations, deaths and treatment episodes. Bayesian autoregressive time‐series analyses were conducted to estimate trend over time: Model 1, no change Model 2, constant rate of change and Model 3, change over time differing in rate after one change point. Past‐year population prevalence of use increased over time. The percentage reporting recent use in sentinel s les increased by 6.1% (95% credible interval [CrI 95% ] 1.2%,16.9% Model 3) per year from around 2017 (48%) until the end of the series (2019: 67%). There was a constant annual increase in number of seizures (count ratio: 1.1, CrI 95% 1.1,1.2) and arrests (1.2, CrI 95% 1.1,1.2), and percentage reporting cocaine as easy to obtain in the sentinel s les (percent increase 1.2%, CrI 95% 0.5%,1.8% Model 2). Cocaine‐related hospitalisation rate increased from 5.1 to 15.6 per 100 000 people from around 2011–2012 to 2017–2018: an annual increase of 1.3 per 100 000 people (CrI 95% 0.8,1.8 Model 3). While the death rate was low (0.23 cocaine‐related deaths per 100 000 people in 2018 Model 2), treatment episodes increased from 3.2 to 5.9 per 100 000 people from around 2016–2017 to 2017–2018: an annual increase of 2.9 per 100 000 people (CrI 95% 1.6,3.7 Model 3). Cocaine use, availability and harm have increased, concentrated in recent years, and accompanied by increased treatment engagement.
Efficient Bayesian Synthetic Likelihood With Whitening Transformations
Publisher: Informa UK Limited
Date: 25-10-2022
Bayesian threshold selection for extremal models using measures of surprise
Publisher: Elsevier BV
Date: 05-2015
Variational Bayes with synthetic likelihood
Publisher: Springer Science and Business Media LLC
Date: 31-08-2018
Efficient hydrological model parameter optimization with Sequential Monte Carlo sampling
Publisher: Elsevier BV
Date: 12-2012
In defence of model-based inference in phylogeography
Publisher: Wiley
Date: 02-2010
A case for a reassessment of the risks of extreme hydrological hazards in the Caribbean
Publisher: Springer Science and Business Media LLC
Date: 07-12-2006
Systematic differences in future 20 year temperature extremes in AR4 model projections over Australia as a function of model skill
Publisher: Wiley
Date: 27-04-2013
DOI: 10.1002/JOC.3500
Nonlinear manifold representation in natural systems: The SOMersault
Publisher: Elsevier BV
Date: 03-2017
A comparative review of dimension reduction methods in approximate bayesian computation
Publisher: Institute of Mathematical Statistics
Date: 05-2013
DOI: 10.1214/12-STS406
Modeling dependence between extreme rainfall and storm surge to estimate coastal flooding risk
Publisher: American Geophysical Union (AGU)
Date: 03-2014
DOI: 10.1002/2013WR014616
Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model
Publisher: Elsevier BV
Date: 02-2017
Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Modeling Extreme Risks in Ecology
Publisher: Wiley
Date: 22-07-2012
DOI: 10.1111/J.1539-6924.2012.01871.X
Abstract: Extreme risks in ecology are typified by circumstances in which data are sporadic or unavailable, understanding is poor, and decisions are urgently needed. Expert judgments are pervasive and disagreements among experts are commonplace. We outline approaches to evaluating extreme risks in ecology that rely on stochastic simulation, with a particular focus on methods to evaluate the likelihood of extinction and quasi-extinction of threatened species, and the likelihood of establishment and spread of invasive pests. We evaluate the importance of assumptions in these assessments and the potential of some new approaches to account for these uncertainties, including hierarchical estimation procedures and generalized extreme value distributions. We conclude by examining the treatment of consequences in extreme risk analysis in ecology and how expert judgment may better be harnessed to evaluate extreme risks.
On sequential Monte Carlo, partial rejection control and approximate Bayesian computation
Publisher: Springer Science and Business Media LLC
Date: 15-02-2012
Developing state and transition models of floodplain vegetation dynamics as a tool for conservation decision‐making: a case study of the Macquarie Marshes Ramsar wetland
Publisher: Wiley
Date: 03-2015
Virus removal by ultrafiltration: Understanding long-term performance change by application of Bayesian analysis
Publisher: Elsevier BV
Date: 10-2017
DOI: 10.1016/J.WATRES.2017.05.057
Abstract: Ultrafiltration is an effective barrier to waterborne pathogens including viruses. Challenge testing is commonly used to test the inherent reliability of such systems. Performance validation seeks to demonstrate the adequate reliability of the treatment system. Appropriate and rigorous data analysis is an essential aspect of validation testing. In this study we used Bayesian analysis to assess the performance of a full-scale ultrafiltration system which was validated and revalidated after five years of operation. A hierarchical Bayesian model was used to analyse a number of similar ultrafiltration membrane skids working in parallel during the two validation periods. This approach enhanced our ability to obtain accurate estimations of performance variability, especially when the s le size of some system skids was limited. This methodology enabled the quantitative estimation of uncertainty in the performance parameters and generation of predictive distributions incorporating those uncertainties. The results indicated that there was a decrease in the mean skid performance after five years of operation of approximately 1 log reduction value (LRV). Interestingly, variability in the LRV also reduced, with standard deviations from the revalidation data being decreased by a mean 0.37 LRV compared with the original validation data. The model was also useful in comparing the operating performance of the various parallel skids within the same year. Evidence of differences was obtained in 2015 for one of the membrane skids. A hierarchical Bayesian analysis of validation data provides robust estimations of performance and the incorporation of probabilistic analysis which is increasingly important for comprehensive quantitative risk assessment purposes.
Exact vs. Approximate Computation: Reconciling Different Estimates of Mycobacterium tuberculosis Epidemiological Parameters
Publisher: Oxford University Press (OUP)
Date: 04-2014
DOI: 10.1534/GENETICS.113.158808
Abstract: Exact computational methods for inference in population genetics are intuitively preferable to approximate analyses. We reconcile two starkly different estimates of the reproductive number of tuberculosis from previous studies that used the same genotyping data and underlying model. This demonstrates the value of approximate analyses in validating exact methods.
Bayesian belief network modelling of chlorine disinfection for human pathogenic viruses in municipal wastewater
Publisher: Elsevier BV
Date: 02-2017
DOI: 10.1016/J.WATRES.2016.11.008
Abstract: Chlorine disinfection of biologically treated wastewater is practiced in many locations prior to environmental discharge or beneficial reuse. The effectiveness of chlorine disinfection processes may be influenced by several factors, such as pH, temperature, ionic strength, organic carbon concentration, and suspended solids. We investigated the use of Bayesian multilayer perceptron (BMLP) models as efficient and practical tools for compiling and analysing free chlorine and monochloramine virus disinfection performance as a multivariate problem. Corresponding to their relative susceptibility, Adenovirus 2 was used to assess disinfection by monochloramine and Coxsackievirus B5 was used for free chlorine. A BMLP model was constructed to relate key disinfection conditions (CT, pH, turbidity) to observed Log Reduction Values (LRVs) for these viruses at constant temperature. The models proved to be valuable for incorporating uncertainty in the chlor(am)ination performance estimation and interpolating between operating conditions. Various types of queries could be performed with this model including the identification of target CT for a particular combination of LRV, pH and turbidity. Similarly, it was possible to derive achievable LRVs for combinations of CT, pH and turbidity. These queries yielded probability density functions for the target variable reflecting the uncertainty in the model parameters and variability of the input variables. The disinfection efficacy was greatly impacted by pH and to a lesser extent by turbidity for both types of disinfections. Non-linear relationships were observed between pH and target CT, and turbidity and target CT, with compound effects on target CT also evidenced. This work demonstrated that the use of BMLP models had considerable ability to improve the resolution and understanding of the multivariate relationships between operational parameters and disinfection outcomes for wastewater treatment.
Genetics and stochastic simulation do mix!
Publisher: Informa UK Limited
Date: 05-2007
Dynamic quantile function models
Publisher: Informa UK Limited
Date: 11-04-2022
A fully probabilistic approach to extreme rainfall modeling
Publisher: Elsevier BV
Date: 03-2003
Estimating global species richness using symbolic data meta‐analysis
Publisher: Wiley
Date: 07-02-2022
DOI: 10.1111/ECOG.05617
Abstract: Global species richness is a key bio ersity metric. Concerns continue to grow over its decline due to overexploitation and habitat destruction by humans. Despite recent efforts to estimate global species richness, the resulting estimates have been highly uncertain and often logically inconsistent. Estimates lower down either the taxonomic or geographic hierarchies are often larger than those above. Further, these estimates have been typically represented in a wide variety of forms, including intervals ( a , b ), point estimates with no uncertainty, and point estimates with either symmetrical or asymmetrical bounds, making it difficult to combine information across different studies. Here, we develop a Bayesian hierarchical approach to estimate global species richness (we estimate 22.02 m species 95% highest posterior density (HPD) interval (10.43 million, 35.28 million)) that combines 50 estimates from published studies. The data mix of intervals and point estimates are reconciled using techniques from symbolic data analysis. This approach allows us to recover interval estimates at each species level, even when data are partially or wholly unobserved, while respecting logical constraints, and to determine the effects of estimation on the whole hierarchy of obtaining future estimates for particular taxa at various levels in the hierarchy.
Simultaneous adjustment of bias and coverage probabilities for confidence intervals
Publisher: Elsevier BV
Date: 02-2014
Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments
Publisher: American Geophysical Union (AGU)
Date: 12-2010
DOI: 10.1029/2010WR009514
Approximate Bayesian computation and Bayes' linear analysis: Toward high- dimensional ABC
Publisher: Informa UK Limited
Date: 02-01-2014
Bayesian calibration and uncertainty analysis of hydrological models: A comparison of adaptive Metropolis and sequential Monte Carlo samplers
Publisher: American Geophysical Union (AGU)
Date: 07-2011
DOI: 10.1029/2010WR010217
The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis
Publisher: Proceedings of the National Academy of Sciences
Date: 25-08-2009
Abstract: The emergence of antibiotic resistance in Mycobacterium tuberculosis has raised the concern that pathogen strains that are virtually untreatable may become widespread. The acquisition of resistance to antibiotics results in a longer duration of infection in a host, but this resistance may come at a cost through a decreased transmission rate. This raises the question of whether the overall fitness of drug-resistant strains is higher than that of sensitive strains—essential information for predicting the spread of the disease. Here, we directly estimate the transmission cost of drug resistance, the rate at which resistance evolves, and the relative fitness of resistant strains. These estimates are made by using explicit models of the transmission and evolution of sensitive and resistant strains of M. tuberculosis , using approximate Bayesian computation, and molecular epidemiology data from Cuba, Estonia, and Venezuela. We find that the transmission cost of drug resistance relative to sensitivity can be as low as 10%, that resistance evolves at rates of ≈0.0025–0.02 per case per year, and that the overall fitness of resistant strains is comparable with that of sensitive strains. Furthermore, the contribution of transmission to the spread of drug resistance is very high compared with acquired resistance due to treatment failure (up to 99%). Estimating such parameters directly from in vivo data will be critical to understanding and responding to antibiotic resistance. For instance, projections using our estimates suggest that the prevalence of tuberculosis may decline with successful treatment, but the proportion of cases associated with resistance is likely to increase.
Functional regression approximate Bayesian computation for Gaussian process density estimation
Publisher: Elsevier BV
Date: 11-2016
Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Exploring the relationship between Aboriginal population indices and fire in Australia over the last 20,000years
Publisher: Elsevier BV
Date: 08-2015
Rapid shifts in dispersal behavior on an expanding range edge Tom
Publisher: Proceedings of the National Academy of Sciences
Date: 29-07-2013
Abstract: Dispersal biology at an invasion front differs from that of populations within the range core, because novel evolutionary and ecological processes come into play in the nonequilibrium conditions at expanding range edges. In a world where species’ range limits are changing rapidly, we need to understand how in iduals disperse at an invasion front. We analyzed an extensive dataset from radio-tracking invasive cane toads ( Rhinella marina ) over the first 8 y since they arrived at a site in tropical Australia. Movement patterns of toads in the invasion vanguard differed from those of in iduals in the same area postcolonization. Our model discriminated enc ed versus dispersive phases within each toad’s movements and demonstrated that pioneer toads spent longer periods in dispersive mode and displayed longer, more directed movements while they were in dispersive mode. These analyses predict that overall displacement per year is more than twice as far for toads at the invasion front compared with those tracked a few years later at the same site. Studies on established populations (or even those a few years postestablishment) thus may massively underestimate dispersal rates at the leading edge of an expanding population. This, in turn, will cause us to underpredict the rates at which invasive organisms move into new territory and at which native taxa can expand into newly available habitat under climate change.
Likelihood-free approximate Gibbs sampling
Publisher: Springer Science and Business Media LLC
Date: 11-03-2020
Inference for stereological extremes
Publisher: Informa UK Limited
Date: 03-2007
Automating and evaluating reversible jump MCMC proposal distributions
Publisher: Springer Science and Business Media LLC
Date: 19-11-2009
Robust evaluation of performance monitoring options for ozone disinfection in water recycling using Bayesian analysis
Publisher: Elsevier BV
Date: 11-2210
DOI: 10.1016/J.WATRES.2017.07.079
Abstract: Ozonation of wastewater has gained popularity because of its effectiveness in removing colour, UV absorbance, trace organic chemicals, and pathogens. Due to the rapid reaction of ozone with organic compounds, dissolved ozone is often not measurable and therefore, the common disinfection controlling parameter, concentration integrated over contact time (CT) cannot be obtained. In such cases, alternative parameters have been shown to be useful as surrogate measures for microbial removal including change in UV
Likelihood-based inference for max-stable processes
Publisher: Informa UK Limited
Date: 03-2010
New models for symbolic data analysis
Publisher: Springer Science and Business Media LLC
Date: 19-09-2023
DOI: 10.1007/S11634-022-00520-8
Abstract: Symbolic data analysis (SDA) is an emerging area of statistics concerned with understanding and modelling data that takes distributional form (i.e. symbols ), such as random lists, intervals and histograms. It was developed under the premise that the statistical unit of interest is the symbol, and that inference is required at this level. Here we consider a different perspective, which opens a new research direction in the field of SDA. We assume that, as with a standard statistical analysis, inference is required at the level of in idual-level data. However, the in idual-level data are unobserved, and are aggregated into observed symbols—group-based distributional-valued summaries—prior to the analysis. We introduce a novel general method for constructing likelihood functions for symbolic data based on a desired probability model for the underlying measurement-level data, while only observing the distributional summaries. This approach opens the door for new classes of symbol design and construction, in addition to developing SDA as a viable tool to enable and improve upon classical data analyses, particularly for very large and complex datasets. We illustrate this new direction for SDA research through several real and simulated data analyses, including a study of novel classes of multivariate symbol construction techniques.
Informing management decisions for ecological networks, using dynamic models calibrated to noisy time‐series data
Publisher: Wiley
Date: 27-01-2020
DOI: 10.1111/ELE.13465
Abstract: Well-intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem-wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision-makers to select interventions. Using these time-series data (sparse and noisy datasets drawn from deterministic Lotka-Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species' future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well-constrained predictions before they can inform decisions that improve environmental outcomes.
Constructing likelihood functions for interval‐valued random variables
Publisher: Wiley
Date: 21-05-2020
DOI: 10.1111/SJOS.12395
Approximate Bayesian computation via regression density estimation
Publisher: Wiley
Date: 05-02-2013
DOI: 10.1002/STA4.15
Logistic Regression Models for Aggregated Data
Publisher: Informa UK Limited
Date: 20-04-2021
Detection of non-stationarity in precipitation extremes using a max-stable process model
Publisher: Elsevier BV
Date: 08-2011
Tail density estimation for exploratory data analysis using kernel methods
Publisher: Informa UK Limited
Date: 03-11-2019
Specifying a hierarchical mixture of experts for hydrologic modeling: Gating function variable selection
Publisher: American Geophysical Union (AGU)
Date: 05-2013
DOI: 10.1002/WRCR.20150
High affinity extremes in combinatorial libraries and repertoires
Publisher: Elsevier BV
Date: 11-2009
DOI: 10.1016/J.JTBI.2009.07.041
Abstract: By generating a large ersity of molecules, the immune system selects antibodies that bind antigens. Sharing the same approach, combinatorial biotechnologies use a large library of compounds to screen for molecules of high affinity to a given target. Understanding the properties of the best binders in the pool aids the design of the library. In particular, how does the maximum affinity increase with the size of the library or repertoire? We consider two alternative models to examine the properties of extreme affinities. In the first model, affinities are distributed lognormally, while in the second, affinities are determined by the number of matches to a target sequence. The second model more explicitly models nucleic acids (DNA or RNA) and proteins such as antibodies. Using extreme value theory we show that the logarithm of the mean of the highest affinity in a combinatorial library grows linearly with the square root of the log of the library size. When there is an upper bound to affinity, this "absolute maximum" is also approached approximately linearly with root log library size, reaching the upper limit abruptly. The design of libraries may benefit from considering how this plateau is reached as the library size is increased.
On bayesian curve fitting via auxiliary variables
Publisher: Informa UK Limited
Date: 2010
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