Bayesian statistics

IV. NAIVE BAYES CLASSIFIERS Naïve Bayes classifier is a simple but effective the Bayesian classifier built upon the strong assumption that different features are independent with each other. Classification is done by selecting the highest posterior of classification variable given a set of feature. Naive Bayes classifiers assume that the effect of a variable value on a given class is independent of the values of other variable. This assumption is called class conditional independence

Dynamic News Classification using Machine Learning Introduction Why this classification is needed ? (Ashutosh) The exponential growth of the data may lead us to a time in future where huge amount of data would not be able to be managed easily. Text Classification is done through Text Mining study which would help sorting the important texts from the content or a document to manage the data or information easily. //Give a scenario, where classification would be mandatory. Advantages of classification

the Institute for Scientific Computation (ISC) at Texas A&M University, I have worked with Professor Bani K. Mallick from the department of statistics and Professor Yalchin Efendiev from the department of mathematics. I have collaborated with researchers in engineering and bio-sciences on developing rigorous uncertainty quantification methods within the Bayesian

Motivation and Objective Human activities like dam construction, dredging, and agricultures cause large amount of sediment transports in rivers, lakes, and estuaries. Erosion and sedimentation is a global issue that tends to be primarily associated with water quality. Pollution by sediment has two major types. For a physical dimension, erosion leads to excessive levels of turbidity in waters and the turbidity limits penetration of sunlight thereby prohibiting growth of algae and rooted aquatic

Forecasting demand and inventory management using Bayesian time series T.A. Spedding University of Greenwich, Chatham Maritime, Kent, UK K.K. Chan Nanyang Technological University, Singapore Batch production, Demand, Forecasting, Inventory management, Bayesian statistics, Time series Keywords Introduction A typical scenario in a manufacturing company in Singapore is one in which all the strategic decisions, including forecasting of future demand, are provided by an overseas office. The

3 Network estimation: graphical model The following projects involve network estimation problems encountered in different biological appli- cations such as gene-gene or protein-protein interaction. The main focus has been on to develop robust, scalable network estimation methodology. Quantile based graph estimation Graphical models are ubiquitous tools to describe the interdependence between variables measured si- multaneously such as large-scale gene or protein expression data. Gaussian graphical

I began a Master’s program at the University of British Columbia School of Population and Public Health last September. This was a culmination of my desire to understand the connections between societal issues and life sciences, and to strengthen my problem solving skills in this regard. In the short time that I have been at the program, I had the chance to understand more about what a career in clinical trials would entail, and to develop the focus of my research thesis at an advanced level. My

are useful for suggesting papers with comparable subjects. Different case of thing net-works can be found in hyperlinks among site pages, motion pictures coordinated by the same executives, et cetera. In this paper, we build up a novel progressive Bayesian model, called Relational Collaborative Topic Regression (RCTR), to join thing relations for suggestion. The principle commitments of RCTR are laid out. II. Foundation: In this area, we give a brief presentation about the back-ground of RCTR, including

by many real-world problems in engineering and life sciences. I have collaborated with researchers in engineering and bio-sciences on developing rigorous uncertainty quantification methods within Bayesian framework for computationally intensive problems. Through developing scalable and multi-level Bayesian methodology, I have worked on estimating heterogeneous spatial fields (e.g., subsurface properties) with multiple scales in dynamical systems. In

“loss function” in decision making, as well as mathematical optimization. It is a common tendency to minimize the loss of the risk models and hence to do so are different methods applicable in today’s statistics. Frequentist expected loss, Bayesian expected loss are mostly used; with Bayesian statistics being the increasingly common methodology in actuarial science. Insurers also make an estimate of the expected claims that arise in the future years, and so they need to hold reserves based on the aggregate