The Experts below are selected from a list of 121839 Experts worldwide ranked by ideXlab platform
Mohammad Reza Meybodi - One of the best experts on this subject based on the ideXlab platform.
-
A streaming Sampling Algorithm for social activity networks using fixed structure learning automata
Applied Intelligence, 2017Co-Authors: Mina Ghavipour, Mohammad Reza MeybodiAbstract:Social activity networks are formed from activities among users (such as wall posts, tweets, emails, and etc.), where any activity between two users results in an addition of an edge to the network graph. These networks are streaming and include massive volume of edges. A streaming graph is considered to be a stream of edges that continuously evolves over time. This paper proposes a Sampling Algorithm for social activity networks, implemented in a streaming fashion. The proposed Algorithm utilizes a set of fixed structure learning automata. Each node of the original activity graph is equipped with a learning automaton which decides whether its corresponding node should be added to the sample set or not. The proposed Algorithm is compared with the best streaming Sampling Algorithm reported so far in terms of Kolmogorov-Smirnov (KS) test and normalized L1 and L2 distances over real-world activity networks and synthetic networks presented as a sequence of edges. The experimental results show the superiority of the proposed Algorithm.
-
A new learning automata‐based Sampling Algorithm for social networks
International Journal of Communication Systems, 2015Co-Authors: Alireza Rezvanian, Mohammad Reza MeybodiAbstract:Summary Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to the whole network data in a reasonable amount of time. Therefore, network Sampling arises to studying the characterization of real networks such as communication, technological, information, and social networks. In this paper, a Sampling Algorithm for complex social networks that is based on a new version of distributed learning automata (DLA) reported recently called extended DLA (eDLA) is proposed. For evaluation purpose, the eDLA-based Sampling Algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for a number of well-known Sampling Algorithms in terms of relative error and Kolmogorov–Smirnov test. It is shown that eDLA-based Sampling Algorithm outperforms the existing Sampling Algorithms. Experimental results further show that the eDLA-based Sampling Algorithm in comparison with the DLA-based Sampling Algorithm has a 26.93% improvement for the average of Kolmogorov–Smirnov value for degree distribution taken over all test networks. Copyright © 2015 John Wiley & Sons, Ltd.
Qian Liu - One of the best experts on this subject based on the ideXlab platform.
-
Asymptotic properties of distributed social Sampling Algorithm
Science China Information Sciences, 2019Co-Authors: Qian Liu, Haitao FangAbstract:Social Sampling is a novel randomized message passing protocol inspired by social communication for opinion formation in social networks. In a typical social Sampling Algorithm, each agent holds a sample from the empirical distribution of social opinions at initial time,and it collaborates with other agents in a distributed manner to estimate the initial empirical distribution by randomly Sampling a message from current distribution estimate. In this paper, we focus on analyzing the theoretical properties of the distributed social Sampling Algorithm over random networks. First, we provide a framework based on stochastic approximation to study the asymptotic properties of the Algorithm. Then, under mild conditions, we prove that the estimates of all agents converge to a common random distribution, which is composed of the initial empirical distribution and the accumulation of quantized error. Besides, by tuning Algorithm parameters, we prove the strong consistency, namely, the distribution estimates of agents almost surely converge to the initial empirical distribution.Furthermore, the asymptotic normality of estimation error generated by distributed social sample Algorithm is addressed. Finally, we provide a numerical simulation to validate the theoretical results of this paper.
-
Asymptotic Efficiency of Distributed Random Sampling Algorithm
2019 Chinese Control Conference (CCC), 2019Co-Authors: Qian Liu, Haitao FangAbstract:In this paper, we focus on estimating the distribution of underlying parameter over random networks through reconstructing the empirical distribution of initial samples, which can be viewed as a particular average consensus problem. A class of quantized communication protocol, in which neighbors exchange information randomly selected based on the current estimate, is considered. To improve the convergence rate of this distributed random Sampling Algorithm, we introduce the Polyak average scheme, and show that asymptotic efficiency can be achieved through the averaging technique under proper conditions. The results show that the minimum limit covariance matrix of estimation error can be reached, i.e., the proposed Algorithm achieves the highest possible rate of convergence. Finally, we provide a numerical simulation to validate the theoretical results of this work.
-
Asymptotic Properties of Distributed Social Sampling Algorithm.
arXiv: Systems and Control, 2019Co-Authors: Qian Liu, Haitao FangAbstract:Social Sampling is a novel randomized message passing protocol inspired by social communication for opinion formation in social networks. In a typical social Sampling Algorithm, each agent holds a sample from the empirical distribution of social opinions at initial time, and it collaborates with other agents in a distributed manner to estimate the initial empirical distribution by randomly Sampling a message from current distribution estimate. In this paper, we focus on analyzing the theoretical properties of the distributed social Sampling Algorithm over random networks. Firstly, we provide a framework based on stochastic approximation to study the asymptotic properties of the Algorithm. Then, under mild conditions, we prove that the estimates of all agents converge to a common random distribution, which is composed of the initial empirical distribution and the accumulation of quantized error. Besides, by tuning Algorithm parameters, we prove the strong consistency, namely, the distribution estimates of agents almost surely converge to the initial empirical distribution. Furthermore, the asymptotic normality of estimation error generated by distributed social Sampling Algorithm is addressed. Finally, we provide a numerical simulation to validate the theoretical results of this paper.
-
Touching Soma Segmentation Based on the Rayburst Sampling Algorithm.
Neuroinformatics, 2017Co-Authors: Qian LiuAbstract:Neuronal soma segmentation is essential for morphology quantification analysis. Rapid advances in light microscope imaging techniques have generated such massive amounts of data that time-consuming manual methods cannot meet requirements for high throughput. However, touching soma segmentation is still a challenge for automatic segmentation methods. In this paper, we propose a soma segmentation method that combines the Rayburst Sampling Algorithm and ellipsoid fitting. The improved Rayburst Sampling Algorithm is used to detect the soma surface; the ellipsoid fitting method then refines jagged sampled soma surface to generate smooth ellipsoidal shapes for efficient analysis. In experiments, we validated the proposed method by applying it to datasets from the fluorescence micro-optical sectioning tomography (fMOST) system. The results indicate that the proposed method is comparable to the manual segmented gold standard with accurate soma segmentation at a relatively high speed. The proposed method can be extended to large-scale image stacks in the future.
Zhaoyuan Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Gibbs-Slice Sampling Algorithm for Estimating the Four-Parameter Logistic Model.
Frontiers in psychology, 2020Co-Authors: Jiwei Zhang, Zhaoyuan ZhangAbstract:The four-parameter logistic (4PL) model has recently attracted much interest in educational testing and psychological measurement. This paper develops a new Gibbs-slice Sampling Algorithm for estimating the 4PL model parameters in a fully Bayesian framework. Here, the Gibbs Algorithm is employed to improve the Sampling efficiency by using the conjugate prior distributions in updating asymptote parameters. A slice Sampling Algorithm is used to update the 2PL model parameters, which overcomes the dependence of the Metropolis-Hastings Algorithm on the proposal distribution (tuning parameters). In fact, the Gibbs-slice Sampling Algorithm not only improves the accuracy of parameter estimation, but also enhances Sampling efficiency. Simulation studies are conducted to show the good performance of the proposed Gibbs-slice Sampling Algorithm and to investigate the impact of different choices of prior distribution on the accuracy of parameter estimation. Based on Markov chain Monte Carlo samples from the posterior distributions, the deviance information criterion and the logarithm of the pseudomarginal likelihood are considered to assess the model fittings. Moreover, a detailed analysis of PISA data is carried out to illustrate the proposed methodology.
Marek J Druzdzel - One of the best experts on this subject based on the ideXlab platform.
-
An Importance Sampling Algorithm Based on Evidence Pre-propagation
arXiv: Artificial Intelligence, 2012Co-Authors: Changhe Yuan, Marek J DruzdzelAbstract:Precision achieved by stochastic Sampling Algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Evidence Pre-propagation Importance Sampling Algorithm (EPIS-BN), an importance Sampling Algorithm that computes an approximate importance function by the heuristic methods: loopy belief Propagation and e-cutoff. We tested the performance of e-cutoff on three large real Bayesian networks: ANDES, CPCS, and PATHFINDER. We observed that on each of these networks the EPIS-BN Algorithm gives us a considerable improvement over the current state of the art Algorithm, the AIS-BN Algorithm. In addition, it avoids the costly learning stage of the AIS-BN Algorithm.
-
ais bn an adaptive importance Sampling Algorithm for evidential reasoning in large bayesian networks
arXiv: Artificial Intelligence, 2011Co-Authors: Jian Cheng, Marek J DruzdzelAbstract:Stochastic Sampling Algorithms, while an attractive alternative to exact Algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance Sampling Algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing Sampling Algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance Sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the Algorithm. We tested the performance of the AIS-BN Algorithm along with two state of the art general purpose Sampling Algorithms, likelihood weighting (Fung and Chang, 1989; Shachter and Peot, 1989) and self-importance Sampling (Shachter and Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, and Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, and Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN Algorithm always performed better than the other two Algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other Algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN Algorithm.
-
An Efficient Sampling Algorithm for Influence Diagrams
2004Co-Authors: Daniel Garcia-sanchez, Marek J DruzdzelAbstract:We describe an efficient Sampling Algorithm for solving influence diagrams that achieves its efficiency by reusing samples for each of the decision strategies. Our Algorithm is exhaustive in the sense of computing the expected utility of each of the possible decision strategies. We show how by a parallel evaluation of all strategies we not only save a significant amount of computation but also produce better quality anytime behavior.
-
An Ecien t Sampling Algorithm for Inuence Diagrams
2004Co-Authors: Daniel Garcia-sanchez, Marek J DruzdzelAbstract:We describe an ecien t Sampling Algorithm for solving inuence diagrams that achieves its eciency by reusing samples for each of the decision strategies. Our Algorithm is exhaustive in the sense of computing the expected utility of each of the possible decision strategies. We show how by a parallel evaluation of all strategies we not only save a signican t amount of computation but also produce better quality anytime behavior.
-
an importance Sampling Algorithm based on evidence pre propagation
Uncertainty in Artificial Intelligence, 2002Co-Authors: Changhe Yuan, Marek J DruzdzelAbstract:Precision achieved by stochastic Sampling Algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Evidence Pre-propagation Importance Sampling Algorithm (EPIS-BN), an importance Sampling Algorithm that computes an approximate importance function using two techniques: loopy belief propagation [19, 25] and e-cutoff heuristic [2]. We tested the performance of EPIS-BN on three large real Bayesian networks: ANDES [3], CPCS [21], and PATHFINDER[11]. We observed that on each of these networks the EPIS-BN Algorithm outperforms AISBN [2], the current state of the art Algorithm, while avoiding its costly learning stage.
Charles E Lawrence - One of the best experts on this subject based on the ideXlab platform.
-
a statistical Sampling Algorithm for rna secondary structure prediction
Nucleic Acids Research, 2003Co-Authors: Ye Ding, Charles E LawrenceAbstract:An RNA molecule, particularly a long-chain mRNA, may exist as a population of structures. Furthermore, multiple structures have been demonstrated to play important functional roles. Thus, a representation of the ensemble of probable structures is of interest. We present a statistical Algorithm to sample rigorously and exactly from the Boltzmann ensemble of secondary structures. The forward step of the Algorithm computes the equilibrium partition functions of RNA secondary structures with recent thermodynamic parameters. Using conditional probabilities computed with the partition functions in a recursive Sampling process, the backward step of the Algorithm quickly generates a statistically representative sample of structures. With cubic run time for the forward step, quadratic run time in the worst case for the Sampling step, and quadratic storage, the Algorithm is efficient for broad applicability. We demonstrate that, by classifying sampled structures, the Algorithm enables a statistical delineation and representation of the Boltzmann ensemble. Applications of the Algorithm show that alternative biological structures are revealed through Sampling. Statistical Sampling provides a means to estimate the probability of any structural motif, with or without constraints. For example, the Algorithm enables probability profiling of single-stranded regions in RNA secondary structure. Probability profiling for specific loop types is also illustrated. By overlaying probability profiles, a mutual accessibility plot can be displayed for predicting RNA:RNA interactions. Boltzmann probability-weighted density of states and free energy distributions of sampled structures can be readily computed. We show that a sample of moderate size from the ensemble of an enormous number of possible structures is sufficient to guarantee statistical reproducibility in the estimates of typical Sampling statistics. Our applications suggest that the Sampling Algorithm may be well suited to prediction of mRNA structure and target accessibility. The Algorithm is applicable to the rational design of small interfering RNAs (siRNAs), antisense oligonucleotides, and trans-cleaving ribozymes in gene knock-down studies.
