National Repository of Grey Literature 34 records found  beginprevious25 - 34  jump to record: Search took 0.01 seconds. 
SBRA method and the development of its application
Půstka, D. ; Marek, Pavel
Tento příspěvek obrací pozornost na potenci l metody SBRA, zvláště u ocelových konstrucí. Plně pravděpodobnostní řešení daného problému je tu ukázáno na dvou příkladech. Na ocelovém nosníku ovlivněném korozí a na akumulaci poškození únavou na ocelových podpěrách namáhaných větrem. 295 This paper turns attention to the potential of the simulation based reliability assessment method SBRA, considering especially the durability assessment of steel structures. A fully probabilistic approach is demonstrated using two examples. A steel component is exposed to time dependent effects of corrosion and assessment of the accumulation fatigue damage.
Application of structure reliability method SBRA - steel frame containing leaning collums
Marek, Pavel ; Václavek, L.
Development of the simulation based probabilistic reliability assessment method SBRA includes the application of the method to the assessment of systems. The discust example is focused on the assessment of a frame containing leaning collums using elastic transformation model (second order theory) the reliability is expresed by comparing probability of failure and target probability.
Load models and analysis of the load effects using simulation method SBRA
Marek, Pavel ; Guštar, M.
Load models and analysis of the load effects using simulation method SBRA.
Assessment Probability of Failure of a Structure Exposed toExtreme Loading
Václavek, L. ; Marek, Pavel
The subject of the paper is the probabilistic safety assessment ofa steel frame containing leaning columns and exposed to several variable loads including earthquake. The assessment is based onthe SBRA method.
Frequentist and Bayesian inference
Shykhmanter, Dmytro ; Vilikus, Ondřej (advisor) ; Hebák, Petr (referee)
The thesis provides both theoretical and practical comparison of frequentist and Bayesian methods of statistical inference. Comparing of these two concepts begins with describing the philosophy of probability theory. Also is introduced the problem of determinism as well as three main probability interpretations. Statistical inference is a process of making general conclusions based on a given evidence. The frequentist statistics uses the observed data as an only evidence for its conclusions, while the Bayesian one is based on an idea that the subjective degree of belief can be also used for these purposes. Why should one disregard to his experience, knowledge or even intuition? Often happens that results of statistical data analysis are useless in sense that they come out not as it is expected. This situation is illustrated when there are a number of ski resorts which are graded on five star scale. If we look to the top ten, we will find that some of those should not belong there, though the data says they do. Generally the top positions are occupied by the objects with fewer reviews, while those with more reviews get lower average score. Bayesian data analysis methods enable to eliminate this kind of problem. Based on a prior information about the whole data set, every ski resort would get a fair score and as the result, the model would better represent the quality of the each resort based on the respondents' reviews.

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