A Bayes interpretation of stacking for M-complete and M-open settings

In M-open problems where no true model can be conceptualized, it is common to back off from modeling and merely seek good prediction. Even in M-complete problems, taking a predictive approach can be very useful. Stacking is a model averaging procedure that gives a composite predictor by combining individual predictors from a list of models using weights that optimize a cross-validation criterion. We show that the stacking weights also asymptotically minimize a posterior expected loss. Hence we formally provide a Bayesian justification for cross-validation. Often the weights are constrained to be positive and sum to one. For greater generality, we omit the positivity constraint and relax the `sum to one' constraint. A key question is `What predictors should be in the average?' We first verify that the stacking error depends only on the span of the models. Then we propose using bootstrap samples from the data to generate empirical basis elements that can be used to form models. We use this in two computed examples to give stacking predictors that are (i) data driven, (ii) optimal with respect to the number of component predictors, and (iii) optimal with respect to the weight each predictor gets.Comment: 37 pages, 2 figure

Weakly Submodular Functions

Submodular functions are well-studied in combinatorial optimization, game theory and economics. The natural diminishing returns property makes them suitable for many applications. We study an extension of monotone submodular functions, which we call {\em weakly submodular functions}. Our extension includes some (mildly) supermodular functions. We show that several natural functions belong to this class and relate our class to some other recent submodular function extensions. We consider the optimization problem of maximizing a weakly submodular function subject to uniform and general matroid constraints. For a uniform matroid constraint, the "standard greedy algorithm" achieves a constant approximation ratio where the constant (experimentally) converges to 5.95 as the cardinality constraint increases. For a general matroid constraint, a simple local search algorithm achieves a constant approximation ratio where the constant (analytically) converges to 10.22 as the rank of the matroid increases

Dementia Education: What are the Needs of Post-Secondary Students in London, Ontario?

Dementia is a chronic and progressive syndrome characterized by the disturbance of multiple brain functions. As of 2008, an estimated 500,000 Canadians will have a dementia diagnosis and is predicted to rise to 1.1 million Canadians in 2038. A lack of dementia awareness has been identified by McCormick Dementia Services. This study examines the current dementia knowledge of a small cross-section of post-secondary students in London, Ontario. A sample size of twenty-eight participants took an online survey in which students identified that they were able to recognize and had sufficient knowledge of dementia. The survey revealed that although adequate knowledge of dementia was present, the participants were unaware of various resources that could be found in their community to further educate themselves outside of the Alzheimer Society. 100% of students think it would be valuable to learn more about dementia. The participants’ expressed that if a youth and dementia education one-day symposium were offered, they were willing to attend on their own accord to further educate themselves. The survey indicated a demand for more opportunities to be made accessible for students to get involved and to gain further understanding of dementia. A more comprehensive study is recommended to revaluate post-secondary student’s interest in a youth and dementia education one-day symposium

THE AR- PROPERTY AND THE FIXED POINT PROPERTY FOR COMPACT MAPS OF A SOME CONVEX SUBSET IN THE SPACE LP(0 < p < 1)

Joint Research on Environmental Science and Technology for the Eart

Using the Bayesian Shtarkov solution for predictions

AbstractThe Bayes Shtarkov predictor can be defined and used for a variety of data sets that are exceedingly hard if not impossible to model in any detailed fashion. Indeed, this is the setting in which the derivation of the Shtarkov solution is most compelling. The computations show that anytime the numerical approximation to the Shtarkov solution is ‘reasonable’, it is better in terms of predictive error than a variety of other general predictive procedures. These include two forms of additive model as well as bagging or stacking with support vector machines, Nadaraya–Watson estimators, or draws from a Gaussian Process Prior

The formal definition of reference priors under a general class of divergence

"May 2014."Dissertation Supervisor: Dr. Dongchu Sun.Includes vita.Bayesian analysis is widely used recently in both theory and application of statistics. The choice of priors plays a key role in any Bayesian analysis. There are two types of priors: subjective priors and objective priors. In practice, however, the difficulties of subjective elicitation and time restrictions frequently limit us to use the objective priors constructed by some formal rules. In this dissertation, our methodology is using reference analysis to derive objective priors. Objective Bayesian inference makes inference depending only on the assumed model and the available data. The prior distribution used to make an inference is least informative in a certain information-theoretic sense. Recently, Berger, Bernardo and Sun (2009) derived reference priors rigorously in the contexts under Kullback-Leibler divergence. In special cases with common support and other regularity conditions, Ghosh, Mergel and Liu (2011) derived a general f-divergence criterion for prior selection. We generalize Ghosh, Mergel and Liu's (2011) results to the case without common support and show how an explicit expression for the reference prior can be obtained under posterior consistency. The explicit expression can be used to derive new reference priors both analytically and numerically.Includes bibliographical references (pages 126-127)

A Bayes Interpretation of Stacking for M-Complete and M-Open Settings

In M-open problems where no true model can be conceptualized, it is common to back off from modeling and merely seek good prediction. Even in M-complete problems, taking a predictive approach can be very useful. Stacking is a model averaging procedure that gives a composite predictor by combining individual predictors from a list of models using weights that optimize a cross validation criterion. We show that the stacking weights also asymptotically minimize a posterior expected loss. Hence we formally provide a Bayesian justification for cross-validation. Often the weights are constrained to be positive and sum to one. For greater generality, we omit the positivity constraint and relax the ‘sum to one’ constraint

Metric compatibility and determination in complete metric spaces

It was established in [8] that Lipschitz inf-compact functions are uniquely determined by their local slope and critical values. Compactness played a paramount role in this result, ensuring in particular the existence of critical points. We hereby emancipate from this restriction and establish a determination result for merely bounded from below functions, by adding an assumption controlling the asymptotic behavior. This assumption is trivially fulfilled if $f$ is inf-compact. In addition, our result is not only valid for the (De Giorgi) local slope, but also for the main paradigms of average descent operators as well as for the global slope, case in which the asymptotic assumption becomes superfluous. Therefore, the present work extends simultaneously the metric determination results of [8] and [18]

Exact Mode Shapes of T-shaped and Overhang-shaped Microcantilevers

Resonance frequencies and mode shapes of microcantilevers are of important interest in micro-mechanical systems for enhancing the functionality and applicable range of the cantilevers in vibration transducing, energy harvesting, and highly sensitive measurement. In this study, using the Euler-Bernoulli theory for beam, we figured out the exact mode shapes of cantilevers of varying widths such as the overhang- or T-shaped cantilevers. The obtained mode shapes have been shown to significantly deviate from the approximate forms of a rectangular cantilever that are commonly used in mechanics and physics. They were then used to figure out the resonance frequencies of the cantilever. The analytical solutions have been confirmed by using the finite element method simulations with very low deviation. This study suggested a method for correctly obtaining the resonance frequency of microcantilevers with complicated dimensions, such as the doubly clamped cantilever with the undercut, with the overhangs at the clamped positions, or with an attached mass in the middle