High-order dynamic Bayesian network learning with hidden common causes for causal gene regulatory network

Background: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes. Results: We have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain. Conclusions: We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed

Essential hidden variables: an introduction and novel algorithm for detection

It has recently been shown that Bayesian networks with hidden variables represent a wider range of probabilistic distributions than Bayesian networks without hidden variables. After introducing the general concept of a hidden variable and how it can be understood in Bayesian networks, we present a distinction between optimizing and essential hidden variables. We propose that it is only essential hidden variables that add representational power to Bayesian networks. We then explain past research with hidden variables in light of this new distinction and implement an exploratory algorithm to find essential hidden variables and to examine the conditions on the distribution that hint at their existence

Stochastische Behandlung von Unsicherheiten in kaskadierten dynamischen Systemen

In dieser Arbeit wird die Idee verfolgt, komplexe Systeme aus sehr einfachen Teilsystemen aufzubauen und für solche Systemkaskaden eine stochastische Zustandsschätzung durchzuführen. Dabei wird die Struktur der Kaskade verwendet, um die Schätzung lokal in den Teilsystemen durchzuführen woraus eine globale Schätzung abgeleitet wird. Im Fokus der Arbeit stehen nichtlineare und hybride Systeme. Als eine Anwendung wird die Intentionserkennung in der Mensch-Roboter-Kooperation betrachtet

De l'indexation d'évènements dans des films (application à la détection de violence)

Dans cette thèse, nous nous intéressons à la détection de concepts sémantiques dans des films "Hollywoodiens" à l'aide de concepts audio et vidéos, dans le cadre applicatif de la détection de violence. Nos travaux se portent sur deux axes : la détection de concepts audio violents, tels que les coups de feu et les explosions, puis la détection de violence, dans un premier temps uniquement fondée sur l'audio, et dans un deuxième temps fondée sur l'audio et la vidéo. Dans le cadre de la détection de concepts audio, nous mettons tout d'abord un problème de généralisation en lumière, et nous montrons que ce problème est probablement dû à une divergence statistique entre les attributs audio extraits des films. Nous proposons pour résoudre ce problème d'utiliser le concept des mots audio, de façon à réduire cette variabilité en groupant les échantillons par similarité, associé à des réseaux Bayésiens contextuels. Les résultats obtenus sont très encourageants, et une comparaison avec un état de l'art obtenu sur les même données montre que les résultats sont équivalents. Le système obtenu peut être soit très robuste vis-à-vis du seuil appliqué en utilisant la fusion précoce des attributs, soit proposer une grande variété de points de fonctionnement. Nous proposons enfin une adaptation de l'analyse factorielle développée dans le cadre de la reconnaissance du locuteur, et montrons que son intégration dans notre système améliore les résultats obtenus. Dans le cadre de la détection de violence, nous présentons la campagne d'évaluation MediaEval Affect Task 2012, dont l'objectif est de regrouper les équipes travaillant sur le sujet de la détection de violence. Nous proposons ensuite trois systèmes pour détecter la violence, deux fondés uniquement sur l'audio, le premier utilisant une description TF-IDF, et le second étant une intégration du système de détection de concepts audio dans le cadre de la détection violence, et un système multimodal utilisant l'apprentissage de structures de graphe dans des réseaux bayésiens. Les performances obtenues dans le cadre des différents systèmes, et une comparaison avec les systèmes développés dans le cadre de MediaEval, montrent que nous sommes au niveau de l'état de l'art, et révèlent la complexité de tels systèmes.In this thesis, we focus on the detection of semantic concepts in "Hollywood" movies using audio and video concepts for the detection of violence. We present experiments in two main areas : the detection of violent audio concepts such as gunshots and explosions, and the detection of violence, initially based only on audio, then based on both audio and video. In the context of audio concepts detection, we first show a generalisation arising between movies. We show that this problem is probably due to a statistical divergence between the audio features extracted from the movies. In order to solve it, we propose to use the concept of audio words, so as to reduce the variability by grouping samples by similarity, combined with contextual Bayesian networks. The results are very encouraging, and a comparison with the state of the art obtained on the same data shows that the results we obtain are equivalent. The resulting system can be either robust against the threshold applied by using early fusion of features, or provides a wide variety of operating points. We finally propose an adaptation of the factor analysis scheme developed in the context of speaker recognition, and show that its integration into our system improves the results. In the context of the detection of violence, we present the Mediaeval Affect Task 2012 evaluation campaign, which aims at bringing together teams working on the topic of violence detection. We then propose three systems for detecting the violence. The first two are based only on audio, the first using a TF-IDF description, and the second being the integration of the previous system for the detection violence. The last system we present is a multimodal system based on Bayesian networks that allows us to explore structure learning algorithms for graphs. The performance obtained in the different systems, and a comparison to the systems developed within Mediaeval, show that we are comparable to the state of the art, and show the complexity of such systems.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Learning the Dimensionality of Hidden Variables

A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden variables poses two problems: determining the relations to other variables in the model and determining the number of states of the hidden variable. In this paper, we address the latter problem in the context of Bayesian networks. We describe an approach that utilizes a score-based agglomerative state-clustering. As we show, this approach allows us to efficiently evaluate models with a range of cardinalities for the hidden variable. We show how to extend this procedure to deal with multiple interacting hidden variables. We demonstrate the effectiveness of this approach by evaluating it on synthetic and real-life data. We show that our approach learns models with hidden variables that generalize better and have better structure than previous approaches.