Discrete Dynamic Bayesian Network Research Articles

Improved brain effective connectivity modelling by dynamic Bayesian networks

Background:If brain effective connectivity network modelling (ECN) could be accurately achieved, early diagnosis of neurodegenerative diseases would be possible. It has been observed in the literature that Dynamic Bayesian Network (DBN) based methods are more successful than others. However, DBNs have not been applied easily and tested much due to computational complexity problems in structure learning. New method:This study introduces an advanced method for modelling brain ECNs using improved discrete DBN (Improved- dDBN) which addresses the computational challenges previously limiting DBN application, offering solutions that enable accurate and fast structure modelling. Results:The practical data and prior sizes needed for the convergence to the globally correct network structure are proved to be much smaller than the theoretical ones using simulated dDBN data. Besides, Hill Climbing is shown to converge to the true structure at a reasonable iteration step size when the appropriate data and prior sizes are used. Finally, importance of data quantization methods are analysed. Comparison with existing methods:The Improved-dDBN method performs better and robust, when compared to the existing methods for realistic scenarios such as varying graph complexity, various input conditions, noise cases and non-stationary connections. The data used in these tests is the simulated fMRI BOLD time series proposed in the literature. Conclusions:Improved-dDBN is a good candidate to be used on real datasets to accelerate developments in brain ECN modelling and neuroscience. Appropriate data and prior sizes can be identified based on the approach proposed in this study for global and fast convergence.

Target threat estimation based on discrete dynamic Bayesian networks with small samples

The accuracy of target threat estimation has a great impact on command decision-making. The Bayesian network, as an effective way to deal with the problem of uncertainty, can be used to track the change of the target threat level. Unfortunately, the traditional discrete dynamic Bayesian network (DDBN) has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing. Considering the finiteness and discreteness of DDBN parameters, a fuzzy k-nearest neighbor (KNN) algorithm based on correlation of feature quantities (CF-FKNN) is proposed for DDBN parameter learning. Firstly, the correlation between feature quantities is calculated, and then the KNN algorithm with fuzzy weight is introduced to fill the missing data. On this basis, a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning. Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing, and improve the effect of DDBN parameter learning in the case of serious sample missing. With the proposed method, the final target threat assessment results are reasonable, which meets the needs of engineering applications.

A practical analysis of sample complexity for structure learning of discrete dynamic Bayesian networks

ABSTRACT Discrete Dynamic Bayesian Network (dDBN) is used in many challenging causal modelling applications, such as human brain connectivity, due to its multivariate, non-deterministic, and nonlinear capability. Since there is not a ground truth for brain connectivity, the resulting model cannot be evaluated quantitatively. However, we should at least make sure that the best structure results for the used modelling approach and the data. Later, this result can be appreciated by further correlated literature of anatomy and physiology. Nearly all of the previously published studies rest on limited data, which brings doubt to the resulting structures. In theory, an immense number of samples is required, which is impossible to collect in practice. In this study, the appropriate number of data which makes a dDBN modelling trustable is searched by practical experiments and found to be for binary and ternary-valued networks, where K is the cardinality of the random variables and p is the maximum number of parents possibly present in the network. If a modelling approach satisfies this amount of data, we can at least say that the resulting structure is trustable.

Distribution line parameter estimation considering dynamic operating states with a probabilistic graphical model

Accurate distribution line parameters are critical for power system control, operation and management. Adjustment of the operating modes makes the estimation of real-time distribution line parameters dynamic and difficult. To calibrate the distribution line parameters from collected operating data, this paper proposes a Discrete Dynamic Bayesian Network (DDBN)-based distribution line parameter estimation model. The maximum likelihood estimation algorithm is utilized to train the DDBN model. The belief propagation (BP) algorithm is used to infer distribution line parameters with the DDBN model. The accuracy of the proposed model and the inference algorithm is investigated by carrying out experiments in a real-world medium voltage distribution network state estimation environment. The experimental results demonstrate that the proposed DDBN model performs better than existing approaches in providing accurate real-time line parameters.

Application of junction tree clustering methods for solving dynamic Bayesian networks probabilistic inference tasks

The article covers clustering algorithms for dynamic Bayesian networks based on the construction of join tree. Investigated problems associated with simplifying the topology of dynamic Bayesian networks using clustering methods and consider various semantic approaches of junction trees construction. The paper presents a junction tree constructing algorithm for the dynamic Bayesian network that takes into account the transition and perception models applied to the process of network formation. Analyzed The relationship between the complete joint probability distribution for the dynamic Bayesian network and the complete joint distribution obtained for the junction tree. It is shown that the required complete joint distribution of the junction tree for the dynamic Bayesian network can be obtained as a product of local probability distributions for each node associated with the resulting junction tree, and proved that this distribution will be equivalent to the probability distribution of the original dynamic Bayesian network. Introduced the main structural approaches to the construction of junction tree for the discrete dynamic Bayesian network and inspected the use of junction tree for solving similar problems for continuous dynamic Bayesian networks with Gaussian and exponential distribution functions of network variables.

Dynamic network model with continuous valued nodes for longitudinal brain morphometry

Longitudinal brain morphometry probes time-related brain morphometric patterns. We propose a method called dynamic network modeling with continuous valued nodes to generate a dynamic brain network from continuous valued longitudinal morphometric data. The mathematical framework of this method is based on state-space modeling. We use a bootstrap-enhanced least absolute shrinkage operator to solve the network-structure generation problem. In contrast to discrete dynamic Bayesian network modeling, the proposed method enables network generation directly from continuous valued high-dimensional short sequence data, being free from any discretization process. We applied the proposed method to a study of normal brain development.

Congested Link Inference Algorithms in Dynamic Routing IP Network

The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB) network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP) and Rest Bayesian Network Model (RBNM), we proposed an Improved CLINK (ICLINK) algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient) to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.

Research on Information Security Risk Assessment Based on Discrete Dynamic Bayesian Network

Being important in the field of information security, it is essential to study the major process and the mathematical models of information security risk assessment.This paper proposed amodel based on the discrete dynamic Bayesian network and further studied the major process of the assessment.The experiment which works on the implementing platform shows the results are reasonable and feasible. The model proves that it iseffective to calculate the probability of the risk.

Programming algorithm for reference of discrete dynamic Bayesian network

The discrete dynamic Bayesian network is a useful tool for modeling and inferring the time series progress,and it has wide modeling application value.But its inference algorithm needs improving.Concerning the shortcomings of the discrete dynamic Bayesian network,such as its inference algorithm is hard to understand,hard to program and running slowly,this paper proposed the most suitable storage data structure of the discrete dynamic Bayesian network,deduced the fast inference algorithm for the discrete dynamic Bayesian network,and verified the inference algorithm for the discrete dynamic Bayesian networks through a sample.

A new dynamic Bayesian network approach for determining effective connectivity from fMRI data

Two techniques based on the Bayesian network (BN), Gaussian Bayesian network and discrete dynamic Bayesian network (DBN), have recently been used to determine the effective connectivity from functional magnetic resonance imaging (fMRI) data in an exploratory manner and to provide a new method for exploring the interactions among brain regions. However, Gaussian BN ignores the temporal relationships of interactions among brain regions, while discrete DBN loses a great deal of information by discretizing the data. To overcome these limitations, the current study proposes a new BN method based on Gaussian assumptions, termed Gaussian DBN, to capture the temporal characteristics of connectivity with less associated loss of information. A set of synthetic data were generated to measure the robustness of this method to noise, and the results were compared with discrete DBN. In addition, real fMRI data obtained from twelve normal subjects in the resting state was used to further demonstrate and validate the effectiveness of the Gaussian DBN method. The results demonstrated that the Gaussian DBN was more robust than discrete DBN and an improvement over BN.

High-resolution genetic mapping with pooled sequencing

BackgroundModern genetics has been transformed by high-throughput sequencing. New experimental designs in model organisms involve analyzing many individuals, pooled and sequenced in groups for increased efficiency. However, the uncertainty from pooling and the challenge of noisy sequencing data demand advanced computational methods.ResultsWe present MULTIPOOL, a computational method for genetic mapping in model organism crosses that are analyzed by pooled genotyping. Unlike other methods for the analysis of pooled sequence data, we simultaneously consider information from all linked chromosomal markers when estimating the location of a causal variant. Our use of informative sequencing reads is formulated as a discrete dynamic Bayesian network, which we extend with a continuous approximation that allows for rapid inference without a dependence on the pool size. MULTIPOOL generalizes to include biological replicates and case-only or case-control designs for binary and quantitative traits.ConclusionsOur increased information sharing and principled inclusion of relevant error sources improve resolution and accuracy when compared to existing methods, localizing associations to single genes in several cases. MULTIPOOL is freely available at http://cgs.csail.mit.edu/multipool/.

Developing an effective algorithm for dynamic UAV path planning with incomplete threat information

In this paper the problem of online dynamic UAV path planning is studied for the situation where a manoeuvring pop-up threat exists and information about this threat is incomplete. The problem is addressed by integrating a structure-varying discrete dynamic Bayesian network (SVDDBN) into the model prediction control (MPC) algorithm. The SVDDBN is used to construct online a dynamic threat assessment model by estimating and predicting the states of the manoeuvring pop-up threat. The output of this model is then fed into the MPC algorithm for effective path planning. The results obtained in the performed simulation studies demonstrate the excellent performance of the proposed algorithm.

Research on the self-defence electronic jamming decision-makingbased on the discrete dynamic Bayesian network

The manner and conditions of running the decision-making system with self-defense electronic jammingare given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making withself-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discretedynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discretedynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesizedifferent targets which are not predominant. In this way, various features at the same time, as well as the samefeature appearing at different time complement mutually; in addition, the accuracy and reliability of electronicjamming decision making are enhanced significantly.

Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophrenia

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal, frontal, and cerebellar regions during an auditory paradigm.

Discrete dynamic Bayesian network analysis of fMRI data.

We examine the efficacy of using discrete Dynamic Bayesian Networks (dDBNs), a data-driven modeling technique employed in machine learning, to identify functional correlations among neuroanatomical regions of interest. Unlike many neuroimaging analysis techniques, this method is not limited by linear and/or Gaussian noise assumptions. It achieves this by modeling the time series of neuroanatomical regions as discrete, as opposed to continuous, random variables with multinomial distributions. We demonstrated this method using an fMRI dataset collected from healthy and demented elderly subjects (Buckner, et al., 2000: J Cogn Neurosci 12:24-34) and identify correlates based on a diagnosis of dementia. The results are validated in three ways. First, the elicited correlates are shown to be robust over leave-one-out cross-validation and, via a Fourier bootstrapping method, that they were not likely due to random chance. Second, the dDBNs identified correlates that would be expected given the experimental paradigm. Third, the dDBN's ability to predict dementia is competitive with two commonly employed machine-learning classifiers: the support vector machine and the Gaussian naive Bayesian network. We also verify that the dDBN selects correlates based on non-linear criteria. Finally, we provide a brief analysis of the correlates elicited from Buckner et al.'s data that suggests that demented elderly subjects have reduced involvement of entorhinal and occipital cortex and greater involvement of the parietal lobe and amygdala in brain activity compared with healthy elderly (as measured via functional correlations among BOLD measurements). Limitations and extensions to the dDBN method are discussed.