Bayesian, statistical approach

In a study of 1230 patients with initially unexplained cardiomyopathies, lithium was implicated in one case (114). Using a data-based mining Bayesian statistical approach to the WHO database of adverse reactions to examine antipsychotic drugs and heart muscle disorders, a significant association was found between lithium and cardiomyopathy, but not myocarditis (146). The authors acknowledged that further study is needed to determine if the association is causal. 

It has recently become more widely appreciated that the presence of rotational diffusional anisotropy in proteins and other macromolecules can have a significant affect on the interpretation of NMR relaxation data in terms of molecular motion. Andrec et al. showed how commonly used NMR relaxation data (Ti, T2 and NOE) obtained at two spectrometer frequencies can be analyzed using a Bayesian statistical approach to reliably detect and... 

Model-based MI is usually performed using a Bayesian statistical approach. Thus, there is a need for specifying a prior distribution on the parameters to carry out... 

ABSTRACT A residual reUabiUty radices of particular and structural members of existing structures subjected to extreme service and climate actions are considered. Time-dependent structural safety margins of particular members and their modifications as stochastic finite sequences are discussed. The primary and revised instantaneous and long-term survival probabilities of members exposed to one and two extreme action effects are analyzed. The revised survival probabdity prediction of members during their residual service life is based on the concepts of truncated resistance distributions and Bayesian statistical approaches. The calculation of revised reliability indices of members is demonstrated by the munerical example. 

SABRE Method. Acronym for Simulated Approach to Bayesian Reliability Evaluation. An advanced approach to designing a reliability test program developed at PicArsn, the objective of which was to design a test program of minimum sample size for artillery fired atomic projectiles. Called the SABRE method, the program uses mathematical modeling, Monte Carlo simulation techniques, and Bayesian statistics. It is a sophisticated system devised to test items that cannot be tested because of their atomic nature. The aim is to determine the risk factor and to predict what will happen when the projectile is fired... 

Many different methods can be applied to virtual screening, and such methods are described in other chapters of this book and/or in the Handbooks of Che-minformatics Here we discuss the methods based on a probabilistic approach. Unfortunately, there are many publications in which the probabilistic or statistical approach items are farfetched. The Binary Kernel Discrimination and the Bayesian Machine Learning Models are actually special... 

With greater sophistication of methods of data acquisition, intuition can play a less important role, and a bayesian philosophical approach becomes more important. In contrast to the classical approach to statistics, which is concerned with the distribution of possible measured values about a unique true value, the bayesian approach is concerned with the distribution of possible true values about the measured value at hand—a concept often greeted with hostility by traditional statisticians. 

The poor statistics seem to call for a different philosophy of interpretation of the evaluated uncertainties. There is increasing attention to the Bayesian statistics [19] and standing discussions take place on the relative merits of various approaches and the philosophy behind them. Modem statistical practice is dominated by two... 

Because both the Bayesian statistics and the above-mentioned method of maximum likelihood operate with the likelihood function, concrete applications of these approaches have some common features. 

Space resolution, may require probabilistic rather than deterministic interpretations and analyses. This type of statistical approach is presented in chapters 7 and 8 and in parts of chapter 13, where some elements of Bayesian statistics are introduced. The inference of causality may then be difficult or impossible, as many mechanisms may lead to the same joint probability distribution of all events in the system. 

Andrec M, Montelione GT, Levy RM (1999) Estimation of dynamic parameters from NMR relaxation data using the Lipari-Szabo model-free approach and Bayesian statistical methods. J Magn Reson 139 408 21... 

The general pharmaceutical physician cannot be expected to be able to generate Bayesian statistical plans for him/herself. These require an experienced statistician and, it may be added, a statistician who is not, him/herself, philosophically opposed to Bayesian rather than frequentist thinking. The decision to employ a Bayesian design for a clinical trial will be viewed as courageous in most companies, and there will be many clinical trials for which an orthodox, frequentist approach will be selected, for several good reasons. Overall, the gen-... 

Bayesian decision theory is a fundamental statistical approach to the problem of classification. This approach is based on quantifying the trade-offs between various classification decisions using probability and the costs that accompany such decisions. It makes the assumption that the decision problem is posed in probabilistic terms and that all of the relevant probability values are known. 

When conducting a quantitative risk assessment the analyst(s) must define its probabilistic basis, and in most cases this means either to use the classical frequency approach or the Bayesian approach. The classical statistical approach is and has been the most commonly used probabilistic basis in health care (Schneider 2006). This approach interprets a probability as the relative fraction of times the event considered occurs if the situation analyzed were hypothetically repeated an infinite munber of times. The imder-lying probability is unknown, and is estimated in the risk analysis. In the alternative, the Bayesian perspective, a probability is a measure of imcertainty about future events and outcomes (consequences), as seen through the eyes ofthe assessor(s) and based on his/her backgroimd information and knowledge at the time of the analysis. Probability is a subjective measure of imcertainty. 

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