Presentations, Tutorials and Archived Program
To cite presentations from the conference, please follow the example below:
Dan Ababei (2015). Non-parametric Bayesian networks in Uninet. In K. Korb (chair) Australasian Bayesian Network Modelling Society Conference, Melbourne, Australia, 2015. See http://abnms.org/conferences/abnms2015/program
Wednesday 25th: Conference Day 1
|9:15||Non-parametric Bayesian networks in Uninet||Dan Ababei (slides, powerpoint)|
In informal logic and argumentation theory the use of graphs to lay out arguments has a long history, beginning arguably with Wigmore’s maps of legal arguments. They have become popular in recent decades, particularly with computer software which makes their creation and management easier. Advocates of argument map software recognize some relation to Bayesian networks (BNs), but claim their tools are easier to understand and use. In response to the obvious value of representing the degrees of strength of relationships between premises and conclusions that BNs provide, they have started using naive Bayes models for analysing arguments. We analyse this in the context of a few simple arguments, including breast cancer diagnosis and the authorship of the Shakespeare corpus, showing that in these and many other cases naive Bayes collapses under the weight of the argument. We review a number of key issues in analysing arguments with BNs, including Jeffrey conditionalization and representing competing hypotheses, and provide reasons for preferring BNs for argumentation to either naive Bayes or qualitative maps. more >
|10:00||ABNMS presidents' welcome||Kevin Korb & Steven Mascaro|
Environmental modelling I
|10:30||Modelling landholder participation in biodiverse carbon plantings using Bayesian belief networks (paper)||Nooshin Torabi, Luis Mata, Ascelin Gordon, Georgia Garrard and Sarah Bekessy|
Carbon farming programs typically aim to maximise landholder participation rates to achieve desired environmental outcomes. This is also critical for programs aiming to tackle both climate change and biodiversity loss simultaneously, as landholder participation in those schemes directly determines the level of carbon sequestered and the potential biodiversity gains. In this study we developed a Bayesian Belief Network (BBN) of landholder participation in biodiverse carbon planting schemes to determine factors most likely to influence program participation. Based on a review of the literature, we developed a conceptual model. The model was refined through interviews with participating landholders, other key stakeholders and, finally, parameterised using expert-elicited knowledge. Our results indicate that participation rates are most influenced by program attractiveness and the identified values of co-benefits (such as biodiversity conservation) rather than financial incentives. Scenario evaluation revealed that providing a combination of biodiversity incentives with more flexible permanence options could increase the program adoption rate. Stacking/ bundling credits combined with contract agreements may also increase the participation rate. These findings can assist policy development by focusing on the aspects of policy design most likely to increase participation rates. more >
|10:55||Application of Bayesian belief networks to identify factors affecting log removal values of microbial indicators through membrane bioreactors (paper)||Trang Trinh, Guido Carvajal, Amos Branch, David Roser, Stuart Khan and Pierre Le-Clech|
In order to provide instruction on what operational conditions need to be set in a validation protocol for membrane bioreactors (MBRs) to ensure appropriately conservative performance testing, factors affecting log removal values (LRV) of microbial indicators by MBRs need to be identified. Microbial indicators of concern in this study include FRNA bacteriophage and somatic coliphage as indicators for virus, Escherichia coli (E. coli) as an indicator for fecal bacteria, and clostridium perfringens as an indicator for protozoa. A large sampling programme was conducted covering 5 full-scale MBRs with various design and operational conditions across Australia. During the full-scale site investigations, the density of microbial indicators in influent, mixed liquor and permeate of the MBRs were assayed, and corresponding sets of operational parameters were also collected. A Bayesian Belief Network (BBN) was constructed from a dataset of 312 cases from which 80% of the data was used to learn the structure and estimate the parameters, and 20% of the data was used for final testing. The network structure was defined by automated structure learning using the bnlearn 3.8.1 package in R. Domain expert knowledge was also introduced in the structure learning in the form of whitelist and backlist. The variables were discretised by density approximation and validation was performed in BayesiaLab 5.4.3. The ROC score was from 70 to 92% for the indicators. The BBN was then used to identify factors influencing LRV of microbial indicators by MBRs by using "Influence path to target" and "Correlation with target node" functions in the BayesiaLab 5.4.3. The results show that membrane age, membrane pore size, permeability and solid retention time are important factors affecting LRV of microbial indicators by MBRs. To the best of the authors’ knowledge, this was the first time that BBN was used for such application. The results have been used to form the basis of the validation protocol for MBRs in Australian water recycling schemes that will ensure consistent accreditation and appreciation of risk. more >
|11:20||A Bayesian belief network analysis to infer an ultrafiltration membrane condition (paper)||Guido Carvajal, David Roser and Stuart Khan|
Ultrafiltration is one the most common unit operations used in the tertiary treatment of wastewater.
From a risk management viewpoint, it is considered an important step because it can be credited with reduction values for pathogens including viruses. Monitoring of ultrafiltration membranes includes measurement of indicator parameters such as turbidity, direct integrity test, permeability and challenge testing. By observing these parameters an operator should be able to infer the condition of the membrane and determine if corrective actions are required. However, there is a lack of available models able to deal with multiple indicators in a probabilistic fashion. Bayesian networks show great potential for addressing this where inference of a condition or state is required from multiple indicator parameters.
|11:45||Dependency patterns for latent variable discovery (slides)||Xuhui Zhang, Kevin Korb, Ann Nicholson and Steven Mascaro|
The causal discovery of Bayesian networks with the presence of latent variables is a popular topic in artificial intelligence, as sources and volumes of data continue to grow with the popularity of Bayesian modelling methods. Causal discovery is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data. Frequently, however, some of those dependencies are generated by causal structures involving variables which have not been measured, i.e., latent variables. Some such patterns of dependency “reveal" themselves, in that no model based solely upon the observed variables can explain them as well as a model using a latent variable. Here we present an algorithm for finding such patterns systematically, so that they may be applied in latent variable discovery in a more rigorous fashion. more >
|12:10||Markov blanket causal discovery using MML (slides)||Yang Li|
Learning a causal network over a set of variables from data is NP-hard and is exponential in the number of variables. State-of-the-art causal discovery algorithms do not scale up well in high-dimensional datasets. Recently, a technique called Markov blanket causal discovery was proposed to increase efficiencies of state-of-the-art algorithms and hence scale up to larger networks. This presentation provides an introduction to causal discovery problems using the Markov blanket technique and minimum message length (MML) to search for the most optimal causal network of a given dataset. more >
Geography & Geology
|13:35||Exploring Bayesian networks as decision support tools in monitoring volcanoes and forecasting eruptions (slides)||Annemarie Christophersen|
Volcanic eruptions are preceded by magma rising to the surface and on its way interacting with surrounding rocks and fluids.
These interactions lead to earthquakes, the release of magmatic gases, and surface deformations. All of these are observed in volcano monitoring and interpreted as precursors to a volcanic eruption.
|14:00||The application of Bayesian networks in urban growth models (slides, powerpoint)||Bradley Rasmussen|
Many local governments and network utilities, such as water providers, face the highly complex process of preparing long term capital works programs and associated financial models to deliver infrastructure to service the future urban growth in their regions.
These capital works usually span 20 years or more and the delivery costs typically run into the billions.
|14:25||Solving surveying problems with multivariate gaussian Bayesian networks (slides)||David Albrecht|
Land surveying offers a rich area of problems.
These problems involve issues such as multiple sources of information, constraints, measurement errors, different precision of measurements, multiple measurements, etc. However, land surveying is a field that has been studied for many years, and over these years surveyors have developed techniques to deal with these issues. So, this provides us with an opportunity to compare different approaches for solving these problems, and to learn from the surveyors techniques ways to deal with these issues in Bayesian Network models.
|15:10||A tool for visualising the output of a DBN for fog forecasting (paper)||Tali Boneh, Xuhui Zhang, Ann Nicholson and Kevin Korb|
|15:35||Driver analysis and product optimization with probabilistic structural equation models (slides [prezi])||Lionel Jouffe and Stefan Conrady|
Structural Equation Modeling (SEM) is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions.
What we call Probabilistic Structural Equation Models (PSEMs) in BayesiaLab are conceptually similar to traditional SEMs. However, PSEMs are based on a Bayesian network structure as opposed to a series of equations.
|17:00||ABNMS board meeting|
Thursday 26th: Conference Day 2
|9:00||Bayesian methods in bioinformatics and ecology||Jonathan Keith|
This talk will discuss applications of Bayesian methods to complex large-scale problems in bioinformatics and ecology. A first application involves identifying new genes and new classes of functional elements in genomes, by segmenting whole genome alignments and simultaneously classifying segments into putative functional classes. A second application involves modelling the spread of the Brisbane fire ant invasion. Both applications involve large data sets and complex hierarchical Bayesian models. more >
Environmental modelling II
|9:45||Modeling the abundance and distribution of a key habitat forming alga in a future ocean (slides)||Lauren Cole, Andy Davis and Trent Penman|
Phyllospora comosa is a key habitat forming alga endemic to south eastern Australia.
Over the last few decades it has disappeared in the urban Sydney area as a result of anthropogenic inputs. As climate change and urbanization continue to alter the ocean environment, the future survival of this species is of concern. A Bayesian belief network is being built to model coastal ocean conditions in order to better understand what influence future changes will have on Phyllospora. This model uses data from a wide variety of published work as well as lab and field experiments. The model also compares the potential for the success of future mitigation options.
|10:10||The role of biodiversity on ecosystem services: systematic-review, mind-mapping and Bayesian network modelling applications (slides)||Marta Pascual|
Even-though the last years have seen a "blossoming" of initiatives and meta-analyses aiming to clarify the link between biodiversity-ecosystem functions-ecosystem services, there is still a need for the integration of research. Few attempts have merged existing knowledge and make it useful for decision-makers. This contribution aims to show new ways of collaboratively representing and modelling these linkages to enable decision-making based on current available knowledge. This is managed through: (i) conducting a systematic-review with contributions describing the links between biodiversity-ecosystem functions-ecosystem services- human well-being; (ii) integrating the information on interactions in a conceptual mind-map; (iii) translating an example of some of these interactions to a Bayesian Network in an effort to demonstrate how already existing and future information could be used and represented when data uncertainty exists. Bayesian Networks allow for a better management of the uncertainty involved in the representation of complex models as well as providing the possibility of creating future scenarios where assumptions might be tested. These models can improve as new data and evidences become available, making predictions more robust through time as more evidence becomes available. Thus, this contribution shows how available knowledge and data can be collated to improve our understanding of complex issues. more >
|10:35||Predicting with fire: using non-parametric Bayesian networks to explore drivers of native mammal occurrence across a heterogeneous forest landscape (paper)||Bronwyn Hradsky, Trent Penman and Julian Di Stefano|
Fire is a key ecological disturbance, shaping biome distribution and community composition globally and affecting faunal distributions via several different pathways. Fire may direct kill individuals, but also causes vegetation change and so indirectly influences the availability of food and shelter. Importantly, these fire-effects occur within a broader environmental context: conditions such as vegetation type and moisture can influence both fire and species distributions. Thus faunal responses to fire often vary between fire events or geographic regions, and time-since-fire is generally a poor predictor of species distributions at a landscape-scale. We investigated whether a Bayesian Network approach could provide an effective way of modelling these interdependent relationships, as regression models are constrained by the need to avoid correlated predictor variables and over-parametrisation. We used motion-sensing cameras to collect native mammal and invasive predator occurrence data at 113 sites across 56,000 ha of continuous eucalypt forest in the Otway Ranges, south-eastern Australia. There was a rainfall and elevation gradient across the study region, with the north-east being substantially flatter, lower and drier than the south-west; time-since-fire ranged from 6 months to 74 years. We developed a conceptual model of the system based on expert opinion and then used Uninet to populate this model as a non-parametric Bayesian Network from our field data. Species occurrences fell into two broad groups, with animals such as swamp wallabies Wallabia bicolor preferring drier vegetation types, less complex habitat and younger forest, but bush rats Rattus fuscipes and long-nosed bandicoots Perameles nasuta being more likely to occur at long-unburnt sites with wet vegetation and high habitat complexity. Fire influenced fauna directly and also through its effects on habitat complexity, woody debris availability and predator occurrence. Our study demonstrates that non-parametric Bayesian Networks are an effective technique for explicitly modelling the complex and context-dependent influence of fire history on faunal distributions. more >
|11:00||A sub-continental analysis quantifying the drivers of fire risk||Trent Penman|
Quantifying the key drivers of fire regimes is fundamental to determining the ability of management to manipulate the risk posed by wildfire to people and property.
Annual area burned by wildfire is driven by four hypothetical switches – biomass, fuel moisture, fire weather and ignitions (Archibald et al. 2009; Bradstock 2010). Each of these is, in turn, influenced by a variety of anthropogenic and natural factors, only some of which can be altered by fire management actions. Many of these factors are not independent, having direct or indirect influences on each other.
|11:45||Using Bayesian networks to predict the risk of forest insect flight activity||Stephen Pawson, Bruce Marcot and Owen Woodberry|
Insect development and activity are weather dependent processes. Flight behaviour, in particular, is strongly mediated by meteorological conditions and the form of this relationship is normally species specific. To predict the activity of forest insects, their flight patterns, and the subsequent likelihood that they may establish a colony on a recently harvested log during a particular time period, requires an understanding of the relationship between key meteorological conditions and flight behaviour. We test key parameters (temperature, humidity, wind speed, rainfall) and their influence on the flight abilities of key forest insect pests (Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus) over a twelve week period at a temporal resolution of 1 hour. We will demonstrate a Bayesian network developed to predict the likelihood of flight activity on the basis of available weather forecast data. We structured Bayesian network models from field data using a TAN naive Bayes approach, and parameterised the probability tables by using case-file training algorithms. Best-performing models were calibrated against the training data with 5.19% overall confusion error, broken down as 10.8% Type I (false positive) error and 4.1% Type II (false negative) error. We discuss some of the theoretical challenges of using Bayesian networks to analyse flight activity data. more >
|12:10||Modelling pest import risk to improve forest health surveillance||Steven Mascaro and Nicolas Meurisse|
The New Zealand Forest Owners Association (NZFOA), in conjunction with the Ministry for Primary Industries (MPI), is in the process of upgrading their current Forest Health Surveillance system to provide better early warning of invasions from a variety of pests that could do harm to New Zealand's plantation forests and, in turn, economy.
As a part of this project, we have developed a model of pest exposure, which spans the export of a pest from a source country to its escape at any possible location within New Zealand. The model is capable of modelling the exposure of a variety of pests across a number of items (including cargo, vehicles and passengers) that can enter the country.
|12:35||Estimating biosecurity risk from introduced flora for pilbara islands using Bayesian networks||Owen Woodberry, Amelia Wenger, Cheryl Lohr and Bob Pressey|
The Pilbara region of Western Australia has 598 islands, many of which have not been properly surveyed. Many of the islands are the last refuges for threatened and endemic species facing numerous threatening processes on the mainland. The islands are also important sites for recreation, cultural activities, and industrial development, mainly for oil and gas extraction. Quarantine and surveillance are arguably two of the more effective tools against the spread of invasive species to islands. Strategies for quarantine and surveillance rely on risk assessment. Effective quarantine would be focused on the most likely source populations of invasive species, and effective surveillance would be designed to cover higher-risk islands more often. Risk assessments are complicated, however, by lack of information on source populations and the diverse vectors by which invasive species reach islands. We used Bayesian Networks (BNs) to assess each island's risk from establishment of invasive species to prioritize quarantine and surveillance activities on the Pilbara Islands. The BNs were based on collated data on island desirability for visitation, recreational visitor load, infrastructure, habitat mapping, and animal dispersal pathways via human movement, wind, ocean currents, and flooded river plumes. Where variables could not be informed by prior scientific research we asked island managers to supply data via expert elicitation. Ultimately, the biosecurity risk profiles generated by the BNs will be incorporated into new decision-support software that is being designed to prioritize a wide variety of island conservation actions. more >
|14:00||Common quandaries and their practical solutions in Bayesian network modeling (paper)||Bruce Marcot|
The ease with which Bayesian network (BN) models can be built had led to some common problems in their construction and interpretation. In this keynote, I address common quandaries and their solutions in BN modeling. Common problems of BN model construction include: assuming that every conditional probability table (CPT) must span [0,100]; use of vague node names and unmeasurable node states; too many parent nodes and little consideration for latent variables; ignoring outlier CPT values; not linking correlated covariates; no peer review of expert-structured BNs; no tests of model calibration or validation; confusing calibration with validation; and "holes" left in CPTs from machine-learning algorithms. Common problems of BN model interpretation include: unclear initial objectives and "model creep;" assuming sensitivity structure from model depth; conflating correlation with causation; conflating proportion with probability; and conflating expectation with probability. Solutions to these various problems pertain to learning correct BN model construction techniques, understanding basics of Bayesian statistical inference, experience with expert knowledge elicitation and necessity of expert peer review and reconciliation, and just being clear with model objectives and variables. more >
|14:45||Omnigram explorer: an interactive tool for understanding Bayesian networks (Slides)||Kevin Korb and Tim Taylor|
We describe the design of Omnigram Explorer (OMG), an open-source tool for the interactive exploration of relationships between variables in Bayesian networks (and other complex systems). OMG is designed to help researchers gain a holistic, qualitative understanding of the relationships between variables, specifically providing interactive, visual support for observational sensitivity analysis. OMG is especially useful for high-lighting dependencies between variables and small groups of variables. It's designed for exploratory analysis of BNs (and other models) and for communicating the salient features of models to non-specialists. more >
|15:10||Improving personal choices with decision networks (slides, powerpoint)||Steven Mascaro, Kevin Korb, Dhananjay Thiruvady and Xuhui Zhang|
The use of Bayesian networks is increasing in many areas of science, research and policy, with decision networks (DN) not far behind.
However, end-user and consumer-oriented modelling has seen much more limited adoption. We haven't yet reached the point of a personalised agent with a DN brain acting as our assistant, filtering information and helping (or even doing) our decision-making --- and it looks as though this may still be some years away. Notwithstanding the brief flurry of interest surrounding BN-based spam email management a few years ago, even unambitious personalised consumer applications of BN agents have yet to surface. Yet the potential for BNs and DNs in this space is substantial.
|15:35||A systems approach to unmanned aircraft expertise||Sandra Johnson and Kerrie Mengersen|
You may also like to check last year's program to see past papers and presentations.
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