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30
Incremental activity modelling in multiple disjoint cameras
 IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—Activity modeling and unusual event detection in a network of cameras is challenging, particularly when the camera views are not overlapped. We show that it is possible to detect unusual events in multiple disjoint cameras as contextincoherent patterns through incremental learning of time ..."
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Abstract—Activity modeling and unusual event detection in a network of cameras is challenging, particularly when the camera views are not overlapped. We show that it is possible to detect unusual events in multiple disjoint cameras as contextincoherent patterns through incremental learning of time delayed dependencies between distributed local activities observed within and across camera views. Specifically, we model multicamera activities using a Time Delayed Probabilistic Graphical Model (TDPGM) with different nodes representing activities in different decomposed regions from different views and the directed links between nodes encoding their time delayed dependencies. To deal with visual context changes, we formulate a novel incremental learning method for modeling time delayed dependencies that change over time. We validate the effectiveness of the proposed approach using a synthetic data set and videos captured from a camera network installed at a busy underground station. Index Terms—Unusual event detection, multicamera activity modeling, time delay estimation, incremental structure learning. Ç 1
Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm
"... In real world applications, graphical statistical models are not only a tool for operations such as classification or prediction, but usually the network structures of the models themselves are also of great interest (e.g., in modeling brain connectivity). The false discovery rate (FDR), the expecte ..."
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Cited by 8 (0 self)
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In real world applications, graphical statistical models are not only a tool for operations such as classification or prediction, but usually the network structures of the models themselves are also of great interest (e.g., in modeling brain connectivity). The false discovery rate (FDR), the expected ratio of falsely claimed connections to all those claimed, is often a reasonable errorrate criterion in these applications. However, current learning algorithms for graphical models have not been adequately adapted to the concerns of the FDR. The traditional practice of controlling the type I error rate and the type II error rate under a conventional level does not necessarily keep the FDR low, especially in the case of sparse networks. In this paper, we propose embedding an FDRcontrol procedure into the PC algorithm to curb the FDR of the skeleton of the learned graph. We prove that the proposed method can control the FDR under a userspecified level at the limit of large sample sizes. In the cases of moderate sample size (about several hundred), empirical experiments show that the method is still able to control the FDR under the userspecified level, and a heuristic modification of the method is able to control the FDR more accurately around the userspecified level. The proposed method is applicable to any models for which statistical tests of conditional independence are available, such as discrete models and Gaussian models.
Computing posterior probabilities of structural features in Bayesian networks
 In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI
, 2009
"... We study the problem of learning Bayesian network structures from data. Koivisto and Sood (2004) and Koivisto (2006) presented algorithms that can compute the exact marginal posterior probability of a subnetwork, e.g., a single edge, in O(n2 n) time and the posterior probabilities for all n(n−1) pot ..."
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Cited by 6 (2 self)
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We study the problem of learning Bayesian network structures from data. Koivisto and Sood (2004) and Koivisto (2006) presented algorithms that can compute the exact marginal posterior probability of a subnetwork, e.g., a single edge, in O(n2 n) time and the posterior probabilities for all n(n−1) potential edges in O(n2 n) total time, assuming that the number of parents per node or the indegree is bounded by a constant. One main drawback of their algorithms is the requirement of a special structure prior that is non uniform and does not respect Markov equivalence. In this paper, we develop an algorithm that can compute the exact posterior probability of a subnetwork in O(3 n) time and the posterior probabilities for all n(n − 1) potential edges in O(n3 n) total time. Our algorithm also assumes a bounded indegree but allows general structure priors. We demonstrate the applicability of the algorithm on several data sets with up to 20 variables. 1
Bayesian Model Averaging Using the kbest Bayesian Network Structures
"... We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the kbest Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the kbest Bayesian networks. We present em ..."
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We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the kbest Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the kbest Bayesian networks. We present empirical results on structural discovery over several real and synthetic data sets and show that the method outperforms the model selection method and the stateoftheart MCMC methods. 1
Belief net structure learning from uncertain interventions
"... We show how to learn causal structure from interventions with unknown effects and/or side effects by adding the intervention variables to the graph and using Bayesian inference to learn the resulting twolayered graph structure. We show that, on a datatset consisting of protein phosphorylation level ..."
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We show how to learn causal structure from interventions with unknown effects and/or side effects by adding the intervention variables to the graph and using Bayesian inference to learn the resulting twolayered graph structure. We show that, on a datatset consisting of protein phosphorylation levels measured under various perturbations, learning the targets of intervention results in models that fit the data better than falsely assuming the interventions are perfect. Furthermore, learning the children of the intervention nodes is useful for such tasks as drug and disease target discovery, where we wish to distinguish direct effects from indirect effects. We illustrate the latter by correctly identifying known targets of genetic mutation in various forms of leukemia using microarray expression data.
Improving Markov Chain Monte Carlo Estimation with AgentBased Models
 Proceedings of the Social Computing, BehavioralCultural Modeling and Prediction SBP 2013
"... Abstract. The Markov Chain Monte Carlo (MCMC) family of methods form a valuable part of the toolbox of social modeling and prediction techniques, enabling modelers to generate samples and summary statistics of a population of interest with minimal information. It has been used successfully to model ..."
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Abstract. The Markov Chain Monte Carlo (MCMC) family of methods form a valuable part of the toolbox of social modeling and prediction techniques, enabling modelers to generate samples and summary statistics of a population of interest with minimal information. It has been used successfully to model changes over time in many types of social systems, including patterns of disease spread, adolescent smoking, and geopolitical conflicts. In MCMC an initial proposal distribution is iteratively refined until it approximates the posterior distribution. However, the selection of the proposal distribution can have a significant impact on model convergence. In this paper, we propose a new hybrid modeling technique in which an agentbased model is used to initialize the proposal distribution of the MCMC simulation. We demonstrate the use of our modeling technique in an urban transportation prediction scenario and show that the hybrid combined model produces more accurate predictions than either of the parent models.
Receptor Tyrosine Kinases Fall into Distinct Classes Based on Their Inferred Signaling Networks
"... These authors contributed equally to this work. Although many anticancer drugs that target receptor tyrosine kinases (RTKs) provide clinical benefit, their longterm use is limited by resistance that is often attributed to increased abundance or activation of another RTK that compensates for the inh ..."
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These authors contributed equally to this work. Although many anticancer drugs that target receptor tyrosine kinases (RTKs) provide clinical benefit, their longterm use is limited by resistance that is often attributed to increased abundance or activation of another RTK that compensates for the inhibited receptor. To uncover common and unique features in the signaling networks of RTKs, we measured timedependent signaling in six isogenic cell lines, each expressing a different RTK as downstream proteins were systematically perturbed by RNA interference. Network models inferred from the data revealed a conserved set of signaling pathways and RTKspecific features that grouped the RTKs into three distinct classes: (i) an EGFR/FGFR1/cMet class constituting epidermal growth factor receptor, fibroblast growth factor receptor 1, and the hepatocyte growth factor receptor cMet; (ii) an IGF1R/NTRK2 class constituting insulinlike growth factor 1 receptor and neurotrophic tyrosine receptor kinase 2; and (iii) a PDGFRβ class constituting plateletderived growth factor receptor β. Analysis of cancer cell
An Efficient Gibbs Sampler for Structural Inference in Bayesian Networks. CRiSM Working Paper 1121
 Dept. of Statistics, University of Warwick). Friedman, N. (2004) Science
, 2011
"... We propose a Gibbs sampler for structural inference in Bayesian networks. The standard Markov chain Monte Carlo (MCMC) algorithms used for this problem are randomwalk MetropolisHastings samplers, but for problems of even moderate dimension, these samplers often exhibit slow mixing. The Gibbs samp ..."
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We propose a Gibbs sampler for structural inference in Bayesian networks. The standard Markov chain Monte Carlo (MCMC) algorithms used for this problem are randomwalk MetropolisHastings samplers, but for problems of even moderate dimension, these samplers often exhibit slow mixing. The Gibbs sampler proposed here conditionally samples the complete set of parents of a node in a single move, by blocking together particular components. These blocks can themselves be paired together to improve the efficiency of the sampler. The conditional distribution used for sampling can be viewed as a posterior distribution for a constrained Bayesian variable selection for the parents of a node. This view sheds further light on the increasingly well understood connection between Bayesian variable selection and structural inference. We empirically examine the performance of the sampler using data simulated from the ALARM network. 1
Annealed Importance Sampling for Structure Learning in Bayesian Networks
"... We present a new sampling approach to Bayesian learning of the Bayesian network structure. Like some earlier sampling methods, we sample linear orders on nodes rather than directed acyclic graphs (DAGs). The key difference is that we replace the usual Markov chain Monte Carlo (MCMC) method by the me ..."
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We present a new sampling approach to Bayesian learning of the Bayesian network structure. Like some earlier sampling methods, we sample linear orders on nodes rather than directed acyclic graphs (DAGs). The key difference is that we replace the usual Markov chain Monte Carlo (MCMC) method by the method of annealed importance sampling (AIS). We show that AIS is not only competitive to MCMC in exploring the posterior, but also superior to MCMC in two ways: it enables easy and efficient parallelization, due to the independence of the samples, and lowerbounding of the marginal likelihood of the model with good probabilistic guarantees. We also provide a principled way to correct the bias due to orderbased sampling, by implementing a fast algorithm for counting the linear extensions of a given partial order.
Open Access
"... Background: Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from e ..."
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Background: Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higherorder dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential. Results: Here we describe a Bayesian network approach that addresses a specific network within a large dataset to discover new components. Our algorithm draws individual genes from a large geneexpression repository, and ranks them as potential members of a known pathway. We apply this method to discover new components of the cAMPdependent protein kinase (PKA) pathway, a central regulator of Dictyostelium discoideum development. The PKA network is well studied in D. discoideum but the transcriptional networks that regulate PKA activity and the transcriptional outcomes of PKA function are largely unknown. Most of the genes highly ranked by our method encode either known components of the PKA pathway or are good candidates. We tested 5 uncharacterized highly ranked genes by creating mutant strains and identified a candidate cAMPresponse elementbinding protein,