Input model uncertainty and reliability
... The proposed research involves: (1) use of copulas to model joint CDFs of input variables; (2) use of the Bayesian method to identify marginal cumulative distribution functions (CDF) and joint CDF of input variables using limited experimental data; (3) reduction of the transformation ordering effect ...
... The proposed research involves: (1) use of copulas to model joint CDFs of input variables; (2) use of the Bayesian method to identify marginal cumulative distribution functions (CDF) and joint CDF of input variables using limited experimental data; (3) reduction of the transformation ordering effect ...
Lecture Notes on Bayesian Nonparametrics Peter Orbanz
... distribution on an infinite-dimensional space T is a stochastic process with paths in T. Such distributions are typically harder to define than distributions on Rd , but we can draw on a large arsenal of tools from stochastic process theory and applied probability. ...
... distribution on an infinite-dimensional space T is a stochastic process with paths in T. Such distributions are typically harder to define than distributions on Rd , but we can draw on a large arsenal of tools from stochastic process theory and applied probability. ...
- Professor JH van Vuuren
... This thesis was initiated as an investigation into the fraud detection and prevention arena after being awarded the opportunity to be involved with the implementation of a fraud management system at one of South Africa’s cellular network operators. Early on in the project it became apparent that Sou ...
... This thesis was initiated as an investigation into the fraud detection and prevention arena after being awarded the opportunity to be involved with the implementation of a fraud management system at one of South Africa’s cellular network operators. Early on in the project it became apparent that Sou ...
COMPUTING A MAXIMAL CLIQUE USING BAYESIAN BELIEF NETWORKS By
... advantages: they provide the same, if not better, flexibility as neural networks; they can be trained; and they overcome the one glaring weakness of neural networks--they have a sound foundation in probability theory. As a result, they are not viewed as black-boxes for computation that “magically” c ...
... advantages: they provide the same, if not better, flexibility as neural networks; they can be trained; and they overcome the one glaring weakness of neural networks--they have a sound foundation in probability theory. As a result, they are not viewed as black-boxes for computation that “magically” c ...
j - Universidad de La Laguna
... La asignación óptima es escaños es, por definición, aquella que proporciona una distribución de escaños más parecida a la distribución de votos en el sentido de minimizar la divergencia entre las distribuciones de probabilidad (de votos y de escaños) a que dan lugar. El resultado, en general, depend ...
... La asignación óptima es escaños es, por definición, aquella que proporciona una distribución de escaños más parecida a la distribución de votos en el sentido de minimizar la divergencia entre las distribuciones de probabilidad (de votos y de escaños) a que dan lugar. El resultado, en general, depend ...
PDF
... procedure of Perali and Chavas involves estimating each equation in unrestricted form using the jackknife technique and then recovering the theoretically restricted demand parameters by minimum distance estimation. We are not aware of any empirical application (except Perali and Chavas) of these rec ...
... procedure of Perali and Chavas involves estimating each equation in unrestricted form using the jackknife technique and then recovering the theoretically restricted demand parameters by minimum distance estimation. We are not aware of any empirical application (except Perali and Chavas) of these rec ...
stat/math 511 probability - University of South Carolina Mathematics
... In probability applications, S will denote a sample space, A will represent an event to which we wish to assign a probability, and ω usually denotes a possible experimental outcome. If ω ∈ A, we would say that “the event A has occurred.” TERMINOLOGY : The null set, denoted as ∅, is the set that cont ...
... In probability applications, S will denote a sample space, A will represent an event to which we wish to assign a probability, and ω usually denotes a possible experimental outcome. If ω ∈ A, we would say that “the event A has occurred.” TERMINOLOGY : The null set, denoted as ∅, is the set that cont ...
Statistics
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as ""all persons living in a country"" or ""every atom composing a crystal"". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.Two main statistical methodologies are used in data analysis: descriptive statistics, which summarizes data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draws conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and a synthetic data drawn from idealized model. An hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a ""false positive"") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a ""false negative""). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other important types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data and/or censoring may result in biased estimates and specific techniques have been developed to address these problems.Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. Statistics continues to be an area of active research, for example on the problem of how to analyze Big data.