Scalable Bayesian Network Learning and its Applications

The Bayesian network is a powerful tool for modeling of cause effect and other uncertain relations between variables in a domain of interest. Probabilistic reasoning with a Bayesian network offers prediction of one or more unobserved variables of interest, given evidence. To use a Bayesian network in a real-world problem, one may need to learn the structure, the parameters, or both from data. However, learning Bayesian networks from high dimensionaland large datasets is a computationally challengingproblem. Parameter learning from large datasets demandsconsiderable computational and memory resources. Moreover, the runtime of theoretically correct structure learning algorithms (such as Hill Climbing, PC) are super-linear in the number of data dimensions.This research develops scalable techniques for both structure learning and parameter learning of Bayesian networks from data. For the parameter learning task, we proposed a novel decomposition of the Expectation Maximization algorithm in the MapReduceframework, where computation is performed in parallel across partitions of the data records. This learning method can handle both complete and incomplete data. Complete data means that all features have values in all records in a dataset, while this is not the case in the incomplete data case. For the Bayesian network structure learning task, a novel score-based method is developed. Score-based structure learning may seems inherently sequential, due to its use of iterative improvement steps. However, we bring parallelism to the scorebased structure learning paradigm.. This is done by organizing the candidate updates for a given structure in a matrix, partitioning the matrix in blocks, and computing scores for each block in parallel. Moreover, we maintain an archive of potential structures, which appear in the search path and use them as starting points of restarts. This mechanism helps preventing the search from getting trapped in local optimal solutions. We apply the proposed techniques to several datasets including two real-world engineering problems: smart building optimizationand next-generation air trac control. For smart buildingoptimization, we study the isolation of candidate causes ofadverse events in a building heating-cooling system. We propose a novel scalable causal learning (SCL) method using Bayesian network structure learning as a central piece. Experimental results on a dataset collected from a Building Automation System (BAS) show improved prediction accuracy and reduced computationtime of SCL compared to existing algorithms. For next generation air traffic control, we improve on the prediction ofaircraft taxi times in airports via Bayesian network uncertainty modeling of surface traffic. We apply both Bayesian network structure learning and parameter learning on dataset obtained from surface trac simulations for three airports in the New York city multiplex: JFK, LGA, and EWR. We use junction tree inference on the trained model to obtain the posterior distribution of transit time variables. The uncertainly model of the transit times are used by a scheduler to minimize the overall delay of departureswhile maximizing runway throughput. Existing approachesheavily rely on subject matter expert models and therefore limited in scope. However, the scalable structure learning approach relies on data, and thereby enables the use of Bayesian networks in any arbitrary airport where prior expert knowledge is limited or unavailable. <br

Circular Antenna Array Synthesis with a Differential Invasive Weed Optimization Algorithm

Abstract — In this article we describe an optimization-based design method for non-uniform, planar, and circular antenna arrays with the objective of achieving minimum side lobe levels for a specific first null beamwidth and also a minimum size of the circumference. Central to our design is a hybridization of two prominent metaheuristics of current interest namely the Invasive Weed Optimization (IWO) and the Differential Evolution (DE). IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. Owing to its superior performance in comparison with many other existing metaheuristics, recently IWO is being used in several engineering design problems from diverse domains. For the present application, we have modified classical IWO by incorporating the difference vector based mutation schemes from the realm of DE. Three difficult instances of the circular array design problem have been presented to illustrate the effectiveness of the hybrid Differential IWO (DIWO) algorithm. The design results obtained with modified IWO have been shown to comfortably outperform the results obtained with other state-of-the-art metaheuristics like Particle Swarm Optimization (PSO), and Differential Evolution (DE) in a statistically significant fashion. Keywords- antenna arrays, circular arrays, sidelobe suppression, real parameter optimization, metaheuistics, invasive weed optimization, particle swarm optimization, differential evolution. I

MapReduce for Bayesian Network Parameter Learning using the EM Algorithm

This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete data. We formulate the classical Expectation Maximization (EM) algorithm within the MapReduce framework. Analytically and experimentally we analyze the speed-up that can be obtained by means of MapReduce. We present details of the MapReduce formulation of EM, report speed-ups versus the sequential case, and carefully compare various Hadoop cluster configurations in experiments with Bayesian networks of different sizes and structures

Accelerating Bayesian Network Parameter Learning Using Hadoop and MapReduce

Learning conditional probability tables of large Bayesian Networks (BNs) with hidden nodes using the Expectation Maximization algorithm is heavily computationally intensive. There are at least two bottlenecks, namely the potentially huge data set size and the requirement for computation and memory resources. This work applies the distributed computing framework MapReduce to Bayesian parameter learning from complete and incomplete data. We formulate both traditional parameter learning (complete data) and the classical Expectation Maximization algorithm (incomplete data) within the MapReduce framework. Analytically and experimentally we analyze the speed-up that can be obtained by means of MapReduce. We present the details of our Hadoop implementation, report speed-ups versus the sequential case, and compare various Hadoop configurations for experiments with Bayesian networks of different sizes and structures. For Bayesian networks with large junction trees, we surprisingly find that MapReduce can give a speed-up compared to the sequential Expectation Maximization algorithm for learning from 20 cases or fewer. The benefit of MapReduce for learning various Bayesian networks is investigated on data sets with up to 1,000,000 records

Polycystic ovary syndrome and its management: In view of oxidative stress

In the past two decades, oxidative stress (OS) has drawn a lot of interest due to the revelation that individuals with many persistent disorders including diabetes, polycystic ovarian syndrome (PCOS), cardiovascular, and other disorders often have aberrant oxidation statuses. OS has a close interplay with PCOS features such as insulin resistance, hyperandrogenism, and chronic inflammation; there is a belief that OS might contribute to the development of PCOS. PCOS is currently recognized as not only one of the most prevalent endocrine disorders but also a significant contributor to female infertility, affecting a considerable proportion of women globally. Therefore, the understanding of the relationship between OS and PCOS is crucial to the development of therapeutic and preventive strategies for PCOS. Moreover, the mechanistic study of intracellular reactive oxygen species/ reactive nitrogen species formation and its possible interaction with women’s reproductive health is required, which includes complex enzymatic and non-enzymatic antioxidant systems. Apart from that, our current review includes possible regulation of the pathogenesis of OS. A change in lifestyle, including physical activity, various supplements that boost antioxidant levels, particularly vitamins, and the usage of medicinal herbs, is thought to be the best way to combat this occurrence of OS and improve the pathophysiologic conditions associated with PCOS