具有区间时滞的离散时间基因调控网络稳定性研究

针对具有随机干扰和区间时滞的离散时间基因调控网络(GRNs),基于Lyapunov稳定性定理,利用改进型自由权矩阵方法研究其时滞相关稳定问题.通过考虑时变时滞、时滞上界及它们的差三者之间的关系,同时保留增广Lyapunov-Krasovskii泛函差分中的所有有用项,获得一种更低保守性的时滞相关渐近稳定新判据.最后,给出仿真实例验证本文方法的有效性及相比已有方法的优越性.     

Stochastic stability for uncertain genetic regulatory networks with interval time-varying delays

This paper is concerned with the robust asymptotic stability analysis for uncertain genetic regulatory networks with both interval time-varying delays and stochastic noise. By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which takes into account the ranges of delays, some new delay-range-dependent and rate-dependent stability criteria are established in terms of linear matrix inequalities (LMIs) to guarantee the delayed genetic regulatory networks to be robustly asymptotically stable in the mean square. As a result, the new criteria are applicable to both fast and slow time-varying delays. Five numerical examples are also used to demonstrate the usefulness of the main results and less conservativeness of the proposed method.     

Bayesian methods for elucidating genetic regulatory networks

Bayesian network methods are useful for elucidating genetic regulatory networks because they can represent more than pair-wise relationships between variables, are resistant to overfitting, and remain robust in the face of noisy data.     

Robust stability for uncertain genetic regulatory networks with interval time-varying delays

This paper addresses the problem of robust stability of uncertain genetic regulatory networks (GRNs) with interval time-varying delays. We derive some new delay-range-dependent and delay-derivative-dependent/independent stability criteria by employing some free-weighting matrices and linear matrix inequalities. In our analysis, we carefully consider the relationship between the time-varying state delays and their bounds when calculating the upper bound of the derivative of Lyapunov functional. We hence show that, the rigorous requirement of other literatures that the time-derivatives of time-varying delays must be smaller than one is abandoned in the proposed scheme. The new criteria are applicable to both fast and slow time-varying delays. Finally, four numerical examples are presented to illustrate the effectiveness and the less conservativeness of the developed results.     

带有时变时滞的切换基因调控网络的稳定性

研究了带有时变时滞的切换基因调控网络的稳定性问题。和现有关于切换基因调控网络结果不同的是,切换基因调控网络包括稳定子系统和不稳定子系统。利用平均驻时方法和线性矩阵不等式技术,得到了带有时变时滞的切换基因调控网络指数稳定的判据。最后的仿真实例验证了结果的有效性。     

Modeling Internet firm survival using Bayesian dynamic models with time-varying coefficients

We showcase a Bayesian dynamic analysis and apply it to a study on the impact of a set of industry, firm and e-commerce-related factors on Internet firm survival. Through the use of one age-based and another calendar time-based dynamic Bayesian model, we are able to examine how the impact of these factors changes over time. Our results are based on data from 115 publicly-traded Internet firms and suggest that Internet firm survival relates to different factors, such as the initial public offerings rate of Internet stocks in the market, financial capital and firm size at different stages in their lifetimes, whose influence may have changed over time.     

Evolving dynamic Bayesian networks with Multi-objective genetic algorithms

A dynamic Bayesian network (DBN) is a probabilistic network that models interdependent entities that change over time. Given example sequences of multivariate data, we use a genetic algorithm to synthesize a network structure that models the causal relationships that explain the sequence. We use a multi-objective evaluation strategy with a genetic algorithm. The multi-objective criteria are a network's probabilistic score and structural complexity score. Our use of Pareto ranking is ideal for this application, because it naturally balances the effect of the likelihood and structural simplicity terms used in the BIC network evaluation heuristic. We use a basic structural scoring formula, which tries to keep the number of links in the network approximately equivalent to the number of variables. We also use a simple representation that favors sparsely connected networks similar in structure to those modeling biological phenomenon. Our experiments show promising results when evolving networks ranging from 10 to 30 variables, using a maximal connectivity of between 3 and 4 parents per node. The results from the multi-objective GA were superior to those obtained with a single objective GA. Brian J. Ross is a professor of computer science at Brock University, where he has worked since 1992. He obtained his BCSc at the University of Manitoba, Canada, in 1984, his M.Sc. at the University of British Columbia, Canada, in 1988, and his Ph.D. at the University of Edinburgh, Scotland, in 1992. His research interests include evolutionary computation, language induction, concurrency, and logic programming. He is also interested in computer applications in the fine arts. Eduardo Zuviria received a BS degree in Computer Science from Brock University in 2004 and a MS degree in Computer Science from Queen's University in 2006 where he held jobs as teacher and research assistant. Currently, he is attending a Ph.D. program at the University of Montreal. He holds a diploma in electronics from a technical college and has worked for eight years in the computer industry as a software developer and systems administrator. He has received several scholarships including the Ontario Graduate Scholarship, Queen's Graduate Scholarship and a NSERC- USRA scholarship.     

Active and dynamic information fusion for multisensor systems with dynamic Bayesian networks.

Many information fusion applications are often characterized by a high degree of complexity because: (1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; (2) decisions must be made efficiently; and (3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources.     

Maritime piracy situation modelling with dynamic Bayesian networks

A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a larger DBN. The application of synthetic data fabrication of maritime vessel behaviour is considered. Behaviour of various vessels in a maritime piracy situation is simulated. A means to integrate information from context based external factors that influence behaviour is provided. Simulated observations of the vessels kinematic states are generated. The generated data may be used for the purpose of developing and evaluating counter-piracy methods and algorithms. A novel methodology for evaluating and optimising behavioural models such as the proposed model is presented. The log-likelihood, cross entropy, Bayes factor and the Bhattacharyya distance measures are applied for evaluation. The results demonstrate that the generative model is able to model both spatial and temporal datasets.     

Design of delay-range-dependent robust controller for uncertain genetic regulatory networks with interval time-varying delays

This paper investigates the problem of robust stabilization for genetic regulatory networks with interval time-varying delays, which are subject to norm-bounded time-varying parameter uncertainties. The time delays including lower and upper bounds of delay are assumed to appear in both the mRNA and protein. The regulatory functions are assumed to be globally Lipschitz continuous. The resulting delay-range-dependent robust controller with interval range is designed in terms of improved bounding technique. A sufficient condition for the solvability of the problem is obtained via a linear matrix inequality (LMI). When this LMI is feasible, an explicit expression of a desired state feedback controller is also given. The theory developed in this paper is demonstrated by two numerical examples.     

New skeleton-based approaches for Bayesian structure learning of Bayesian networks

Automatically learning the graph structure of a single Bayesian network (BN) which accurately represents the underlying multivariate probability distribution of a collection of random variables is a challenging task. But obtaining a Bayesian solution to this problem based on computing the posterior probability of the presence of any edge or any directed path between two variables or any other structural feature is a much more involved problem, since it requires averaging over all the possible graph structures. For the former problem, recent advances have shown that search + score approaches find much more accurate structures if the search is constrained by a previously inferred skeleton (i.e. a relaxed structure with undirected edges which can be inferred using local search based methods). Based on similar ideas, we propose two novel skeleton-based approaches to approximate a Bayesian solution to the BN learning problem: a new stochastic search which tries to find directed acyclic graph (DAG) structures with a non-negligible score; and a new Markov chain Monte Carlo method over the DAG space. These two approaches are based on the same idea. In a first step, both employ a previously given skeleton and build a Bayesian solution constrained by this skeleton. In a second step, using the preliminary solution, they try to obtain a new Bayesian approximation but this time in an unconstrained graph space, which is the final outcome of the methods. As shown in the experimental evaluation, this new approach strongly boosts the performance of these two standard techniques proving that the idea of employing a skeleton to constrain the model space is also a successful strategy for performing Bayesian structure learning of BNs.     

Mean square exponential stability of stochastic genetic regulatory networks with time-varying delays

In this paper, we study the mean square exponential stability of stochastic genetic regulatory networks with time-varying delays. Two kinds of time-varying delays are considered: one is differentiable with bounded delay derivative the other is continuous without constraints on the delay derivative. In order to investigate the mean square exponential stability in stochastic genetic regulatory networks, some novel rate-dependent/independent mean square exponential stability criteria are derived by constructing Lyapunov-Krasovskii functional. The sufficient conditions are given in terms of linear matrix inequalities. Moreover, illustrative examples are used to substantiate the effectiveness and less conservativeness of our results.     

Active inference for dynamic Bayesian networks with an application to tissue engineering

In temporal domains, agents need to actively gather information to make more informed decisions about both the present and the future. When such a domain is modeled as a temporal graphical model, what the agent observes can be incorporated into the model by setting the respective random variables as evidence. Motivated by a tissue engineering application where the experimenter needs to decide how early a laboratory experiment can be stopped so that its possible future outcomes can be predicted within an acceptable uncertainty, we first present a dynamic Bayesian network (DBN) model of vascularization in engineered tissues and compare it with both real-world experimental data and agent-based simulations. We then formulate the question of “how early an experiment can be stopped to guarantee an acceptable uncertainty about the final expected outcome” as an active inference problem for DBNs and empirically and analytically evaluate several search algorithms that aim to find the ideal time to stop a tissue engineering laboratory experiment.     

非线性非齐次Markov跳变系统的贝叶斯滤波

针对具有时变不确定转移概率的非线性非齐次Markov跳变系统, 提出一种贝叶斯状态估计方法.该方法首次采用带约束高斯概率密度函数来刻画转移概率的真实特性. 然后,基于参考概率空间法, 将实际的概率测度投影到理想概率空间, 得出信息变量的递归表达式. 同时, 在贝叶斯框架内给出转移概率矩阵的最大后验估计式. 进一步, 采用粒子逼近法求解转移概率矩阵的最大后验估计, 解决非线性函数的多重积分问题, 进而获取状态估计值. 最后, 通过一个仿真示例表明该方法的有效性.     

具有传递变量的动态贝叶斯网络结构学习

针对现有学习方法对完全时间不对称数据的动态贝叶斯网络学习不具有实用性,提出一种借助传递变量进行完全时间不对称数据的动态贝叶斯网络结构学习方法.首先进行相邻时间片间的传递变量序列学习;然后,基于节点排序和局部打分一搜索,进行动态贝叶斯网络局部结构学习;最后通过时序扩展得到整个动态贝叶斯网络结构.     

Articulatory feature recognition using dynamic Bayesian networks

We describe a dynamic Bayesian network for articulatory feature recognition. The model is intended to be a component of a speech recognizer that avoids the problems of conventional “beads-on-a-string” phoneme-based models. We demonstrate that the model gives superior recognition of articulatory features from the speech signal compared with a state-of-the-art neural network system. We also introduce a training algorithm that offers two major advances: it does not require time-aligned feature labels and it allows the model to learn a set of asynchronous feature changes in a data-driven manner.     

Discovering excitatory relationships using dynamic Bayesian networks

Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human–computer interaction modeling. In this paper, we introduce the notion of excitatory networks which are essentially temporal models where all connections are stimulative, rather than inhibitive. The emphasis on excitatory connections facilitates learning of network models by creating bridges to frequent episode mining. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and that such relationships can be summarized into a dynamic Bayesian network (DBN). This leads to an algorithm that is significantly faster than state-of-the-art methods for inferring DBNs, while simultaneously providing theoretical guarantees on network optimality. We demonstrate the advantages of our approach through an application in neuroscience, where we show how strong excitatory networks can be efficiently inferred from both mathematical models of spiking neurons and several real neuroscience datasets.