Bayesian Learning Essay examples - 1308 Words

BAYESIAN LEARNING Abstract Uncertainty has presented a difficult obstacle in artificial intelligence. Bayesian learning outlines a mathematically solid method for dealing with uncertainty based upon Bayes Theorem. The theory establishes a means for calculating the probability an event will occur in the future given some evidence based upon prior occurrences of the event and the posterior probability that the evidence will predict the event. Its use in artificial intelligence has been met with success in a number of research areas and applications including the development of cognitive models and neural networks. At the same time, the theory has been criticized for being philosophically unrealistic and logistically inefficient.†¦show more content†¦They allow intelligent systems flexibility and a logical way to update their database of knowledge. The appeal of probability theories in AI lies in the way they express the qualitative relationship among beliefs and can process these relationships to draw c onclusions (Pearl, 1988). One of the most formalized probabilistic theories used in AI relates to Bayes theorem. Bayesian methods have been used for a variety of AI applications across many disciplines including cognitive modeling, medical diagnosis, learning causal networks, and finance. Two years after his death, in 1763, Rev. Thomas Bayes Essay Toward solving a Problem in the Doctrine of Chances was published. Bayes is regarded as the first to use probability inductively and established a mathematical basis for probability inference which he outlined in this now famous paper. The idea behind Bayes method is simple; the probability that an event will occur in future trials can be calculated from the frequency with which it has occurred in prior trails. Lets consider some everyday knowledge to outline Bayes rule: where theres smoke, theres fire. We use this everyday cliche to suggest cause and effect. But how are such relationships learned in and from everyday experience? Conditional probability provides a way to estimate the likelihood of some outcome given a particular situation. Bayes theorem further refines this idea by incorporatingShow MoreRelatedResearch Statement : Texas A M University1438 Words   |  6 Pagesto inverse problems, transport based filtering, graphical models and online learning. My research projects are motivated 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.gRead MoreOnline Learning : Stochastic Approximation1139 Words   |  5 Pages4 Online learning: Stochastic Approximation Estimating the mixing density of a mixture distribution remains an interesting problem in the statistics literature. Stochastic approximation (SA) provides a fast recursive way for numerically maximizing a function under measurement error. Using suitably chosen weight/step-size the stochastic approximation algorithm converges to the true solution, which can be adapted to estimate the components of the mixing distribution from a mixture, in the form of recursivelyRead MoreNetwork Estimation : Graphical Model1222 Words   |  5 Pagesdependence structures using precision matrices and they are generated under a multivariate normal joint distribution. However, they suffer from several shortcomings since they are based on Gaussian distribution assumptions. We have developed [2] a novel Bayesian quantile based approach for sparse estimation of graphs. The resulting graph estimation is robust to outliers and 3 applicable under general distributional assumptions. In the theoretical development, the graph estimation consistency result is alsoRead MoreComparative Study Of Classification Algorithms3008 Words   |  13 Pagesclassification [3]. III. NAà VE BAYES CLASSIFIER Naà ¯ve Bayes classifier is based on Bayes theorem. It’s a baseline classification algorithm. Naà ¯ve Bayes classifier assumes that the classes for classification are independent. Though this is rarely true Bayesian classification has shown that there are some theoretical reasons for this apparent unreasonable efficiency. There are various proofs that show that even though the probability estimates of Naà ¯ve Bayes classification are low it delivers quite goodRead MoreI Am A Master s Program At The University Of British Columbia School Of Population And Public Health1717 Words   |  7 Pagesand its commitment to cultural, experiential, and international diversity reflects my ideal learning environment. Moreover, the curriculum will equip me with the knowledge and hands-on experience needed to be an effective and responsible researcher for clinical trials. In terms of my research interests, I am particularly interested in a more in-depth study of Bayesian adaptive clinical trials. The Bayesian approach is something we employ in our everyday interactions and decision-making. For exampleRead MoreDemand Inventory Management4997 Words   |  20 PagesForecasting 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. TheRead MoreEssay On Sentiment Classification769 Words   |  4 Pagesparticular aspect into consideration. There can be a situation where the sentiment holder may express contrasting sentiments for the same product, object, organization etc Techniques for sentiment analysis is generally partitioned as (1) machine learning approach, (2) lexicon-based approach and (3) combined approach (Meddhat et al., 2014a). There are two approaches for the lexicon-based approach. First one is the dictionarybased approach and the second one is the corpus-based approach that utilizesRead MoreThe Static Model Of Data Mining Essay1710 Words   |  7 Pagesbased on COQUALMO are useful in facilitating the right balance of activities to meet quality goals.The static model based on product and project metrics and used Bayesian Belief network model and AgenaRisk tool are used [10,11]. Other researches regarding defect models include regression models [12,13], statistical models and machine learning based models [14,15]. This includes artificial neural networks, instance-based reasoning, decision trees, and rule inductions. Many techniques are used as enhancementsRead MoreA Review On Thing Net Works970 Words   |  4 Pagesare 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, includingRead MoreMethodology of the Naà ¯ve Bayes Algorithm. Essay1534 Words   |  7 Pagesthe presence of many other words. To make our life easier, we make an assumption that all occurrences are independent of each other (thus the model is called â€Å"naive†). The entire Naà ¯ve Bayess rule without the â€Å"independence† assumption is called â€Å"Bayesian Network†. We do not intend to go into that in this project. Bayess rule for multiple evidences: P (E1, E2... En | H) x P (H) P (H | E1, E2... En) = _____________________ P (E1, E2... En) With the assumption of independence, we can rephrase