A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations

A Bayesian network is a probabilistic graphical model that consists of a directed acyclic graph (DAG), where each node is a random variable and attached to each node is a conditional probability distribution (CPD). A Bayesian network can be learned from data using the well-known score-and-search approach, and within this approach a key consideration is how to simultaneously learn the global structure in the form of the underlying DAG and the local structure in the CPDs. Several useful forms of local structure have been identified in the literature but thus far the score-and-search approach has only been extended to handle local structure in form of context-specific independence. In this paper, we show how to extend the score-and-search approach to the important and widely useful case of noisy-OR relations. We provide an effective gradient descent algorithm to score a candidate noisy-OR using the widely used BIC score and we provide pruning rules that allow the search to successfully scale to medium sized networks. Our empirical results provide evidence for the success of our approach to learning Bayesian networks that incorporate noisy-OR relations.Comment: Accepted to Probabilistic Graphical Models, 202

Potential of cone penetrating testing for mapping deeply buried palaeolandscapes in the context of archaeological surveys in polder areas

Geoarchaeological mapping of wetlands conventionally involves extensive coring. Especially in wetlands marked by a deep palaeosurface (>3 m deep) this can be very difficult and time-consuming. In this paper we therefore present an alternative approach based on Cone Penetration Testing (CPT) for structured, rapid and cost-effective evaluation of buried palaeolandscapes. Both estuarine and river floodplain environments were investigated, including the watereland transition zone (marsh). The efficiency, reliability and repeatability of the CPT method was tested through the comparison with ground-truth core data. The CPT data generally allowed highly accurate mapping of the palaeotopography of the prehistoric surfaces and the overlying peat sequences. Thin organic-rich clay intercalations within the peat layers could often still be identified. Additional pore pressure, conductivity and seismic velocity data (from CPTU, CPT-C and S-CPT) did not add much crucial information and their main use seems to lie in the added value for near surface geophysical measurements. The results of this research clearly illustrate the importance of CPT information for mapping of palaeolandscapes in archaeology

DYNAMIC PROBABILITY FAILURE USING BAYESIAN NETWORK FOR HYDROGEN INFRASTRUCTURE MODELING

To produce large scale hydrogen production, it requires adequate and efficient risk control. For decades, fault tree analysis was the most widely used tool for risk assessment for industrial sector generally and hydrogen infrastructure particularly in terms of risk and consequences associated to it. The limitation to this tool is it tends to be static and do not develop over time which can give unreliable estimation of risk. The purpose of this project is to study the suitability and efficiency of dynamic Bayesian Networks in terms of projecting the risk probability failure that develop over time for hydrogen infrastructure as the alternative of the fault tree analysis. In this study, only the risk probability failure is covered without further exploration on the consequences of the risk. The process involved by the conversion of fault tree to Bayesian Networks model by using appropriate framework. Then, the conditional probability table is assigned to each node where the numbers of CPT depend on the numbers of relationship between nodes. Finally the temporal reasoning is done to show the time-invariant between each node and the beliefs is updated to get the results. The ways of inference use for this study are filtering and smoothing. The results show that generally, the OR gates contribute to higher risk probability compare to AND gates. Besides that, the probability for hydrogen activities increase from year to year with the assumption the accident did not happen the previous year. In addition, the instantaneous release incident is relatively low and unlikely to happen compare to the continuous release

Influence-Optimistic Local Values for Multiagent Planning --- Extended Version

Recent years have seen the development of methods for multiagent planning under uncertainty that scale to tens or even hundreds of agents. However, most of these methods either make restrictive assumptions on the problem domain, or provide approximate solutions without any guarantees on quality. Methods in the former category typically build on heuristic search using upper bounds on the value function. Unfortunately, no techniques exist to compute such upper bounds for problems with non-factored value functions. To allow for meaningful benchmarking through measurable quality guarantees on a very general class of problems, this paper introduces a family of influence-optimistic upper bounds for factored decentralized partially observable Markov decision processes (Dec-POMDPs) that do not have factored value functions. Intuitively, we derive bounds on very large multiagent planning problems by subdividing them in sub-problems, and at each of these sub-problems making optimistic assumptions with respect to the influence that will be exerted by the rest of the system. We numerically compare the different upper bounds and demonstrate how we can achieve a non-trivial guarantee that a heuristic solution for problems with hundreds of agents is close to optimal. Furthermore, we provide evidence that the upper bounds may improve the effectiveness of heuristic influence search, and discuss further potential applications to multiagent planning.Comment: Long version of IJCAI 2015 paper (and extended abstract at AAMAS 2015

Approximate Bayesian Network Formulation for the Rapid Loss Assessment of Real-World Infrastructure Systems

This paper proposes to learn an approximate Bayesian Network (BN) model from Monte-Carlo simulations of an infrastructure system exposed to seismic hazard. Exploiting preliminary physical simulations has the twofold benefit of building a drastically simplified BN and of predicting complex system performance metrics. While the approximate BN cannot yield exact probabilities for predictive analyses, its use in backward analyses based on evidenced variables yields promising results as a decision support tool for post-earthquake rapid response. Only a reduced set of infrastructure components, whose importance is ranked through a random forest algorithm, is selected to predict the performance of the system. Further, owing to the higher importance of evidenced nodes, the ranking method is enhanced with a recursive evidence-driven BN-building algorithm, which iteratively inserts evidenced components into the subset identified by the random forest algorithm. This approach is applied to a French road network, where only 5 to 10 components out of 58 are kept to estimate the distribution of system performance metrics that are based on traffic flow. Sensitivity studies on the number of selected components, the number of off-line simulation runs and the discretization of variables reveal that the reduced BN applied to this specific example generates trustworthy estimates

Cone Penetration Testing 2022

This volume contains the proceedings of the 5th International Symposium on Cone Penetration Testing (CPT’22), held in Bologna, Italy, 8-10 June 2022. More than 500 authors - academics, researchers, practitioners and manufacturers – contributed to the peer-reviewed papers included in this book, which includes three keynote lectures, four invited lectures and 169 technical papers. The contributions provide a full picture of the current knowledge and major trends in CPT research and development, with respect to innovations in instrumentation, latest advances in data interpretation, and emerging fields of CPT application. The paper topics encompass three well-established topic categories typically addressed in CPT events: - Equipment and Procedures - Data Interpretation - Applications. Emphasis is placed on the use of statistical approaches and innovative numerical strategies for CPT data interpretation, liquefaction studies, application of CPT to offshore engineering, comparative studies between CPT and other in-situ tests. Cone Penetration Testing 2022 contains a wealth of information that could be useful for researchers, practitioners and all those working in the broad and dynamic field of cone penetration testing

Building Bayesian Networks: Elicitation, Evaluation, and Learning

As a compact graphical framework for representation of multivariate probabilitydistributions, Bayesian networks are widely used for efficient reasoning underuncertainty in a variety of applications, from medical diagnosis to computertroubleshooting and airplane fault isolation. However, construction of Bayesiannetworks is often considered the main difficulty when applying this frameworkto real-world problems. In real world domains, Bayesian networks are often built by knowledge engineering approach. Unfortunately, eliciting knowledge from domain experts isa very time-consuming process, and could result in poor-quality graphicalmodels when not performed carefully. Over the last decade, the research focusis shifting more towards learning Bayesian networks from data, especially withincreasing volumes of data available in various applications, such asbiomedical, internet, and e-business, among others.Aiming at solving the bottle-neck problem of building Bayesian network models, thisresearch work focuses on elicitation, evaluation and learning Bayesiannetworks. Specifically, the contribution of this dissertation involves the research in the following five areas:a) graphical user interface tools forefficient elicitation and navigation of probability distributions, b) systematic and objective evaluation of elicitation schemes for probabilistic models, c)valid evaluation of performance robustness, i.e., sensitivity, of Bayesian networks,d) the sensitivity inequivalent characteristic of Markov equivalent networks, and the appropriateness of using sensitivity for model selection in learning Bayesian networks,e) selective refinement for learning probability parameters of Bayesian networks from limited data with availability of expert knowledge. In addition, an efficient algorithm for fast sensitivity analysis is developed based on relevance reasoning technique. The implemented algorithm runs very fast and makes d) and e) more affordable for real domain practice

Dynamic Trees: A Hierarchical Probabilistic Approach to Image Modelling

Institute for Adaptive and Neural ComputationThis work introduces a new class of image model which we call dynamic trees or DTs. A dynamic tree model specifies a prior over structures of trees, each of which is a forest of one or more tree-structured belief networks (TSBN). In the literature standard tree-structured belief network models were found to produce “blocky” segmentations when naturally occurring boundaries within an image did not coincide with those of the subtrees in the rigid fixed structure of the network. Dynamic trees have a flexible architecture which allows the structure to vary to accommodate configurations where the subtree and image boundaries align, and experimentation with the model showed significant improvements. They are also hierarchical in nature allowing a multi-scale representation and are constructed within a well founded Bayesian framework. For large models the number of tree configurations quickly becomes intractable to enumerate over, presenting a problem for exact inference. Techniques such as Gibbs sampling over trees are considered and search using simulated annealing finds high posterior probability trees on synthetic 2-d images generated from the model. However simulated annealing and sampling techniques are rather slow. Variational methods are applied to the model in an attempt to approximate the posterior by a simpler tractable distribution, and the simplest of these techniques, mean field, found comparable solutions to simulated annealing in the order of 100 times faster. This increase in speed goes a long way towards making real-time inference in the dynamic tree viable. Variational methods have the further advantage that by attempting to model the full posterior distribution it is possible to gain an indication as to the quality of the solutions found. An EM-style update based upon mean field inference is derived and the learned conditional probability tables (describing state transitions between a node and its parent) are compared with exact EM on small tractable fixed architecture models. The mean field approximation by virtue of its form is biased towards fully factorised solutions which tends to create degenerate CPTs, but despite this mean field learning still produces solutions whose log likelihood rivals exact EM. Development of algorithms for learning the probabilities of the prior over tree structures completes the dynamic tree picture. After discussion of the relative merits of certain representations for the disconnection probabilities and initial investigation on small model structures the full dynamic tree model is applied to a database of images of outdoor scenes where all of its parameters are learned. DTs are seen to offer significant improvement in performance over the fixed architecture TSBN and in a coding comparison the DT achieves 0 294 bits per pixel (bpp) compression compared to 0 378 bpp for lossless JPEG on images of 7 colours