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Bayesian statistical inference
Statistica, 2017 This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D.
Bruno De Finetti
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Aspects about Statistical Inference [PDF]
Theoretical and Applied Economics, 2006 There are two major types of data sources that can be used when a phenomenon or a variable is investigated: the population and the sample. Based upon the sample, various facts concerning the entire population can be deduced; this process is called ...
Georgeta Vintila +2 more
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Improving statistical inference with uncertain non-sample prior information [PDF]
, 2015 In the classical inference, the observed sample data is the only source of information. The Bayesian inferential methods assume prior distribution of the underlying model parameters to combine with sample data.
Yunus, Rossita M. +3 more
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Measurement error and statistical disclosure control [PDF]
, 2010 Statistical agencies release microdata to researchers after applying statistical disclosure control (SDC) methods. Noise addition is a perturbative SDC method which is carried out by adding independent random noise to a continuous variable or by ...
Shlomo, Natalie, Natalie Shlomo
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, 2009 Often scientific information on various data generating processes are presented in the from of numerical and categorical data. Except for some very rare occasions, generally such data represent a small part of the population, or selected outcomes of any ...
Shahjahan Khan, Khan, Shahjahan
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An introduction to the maximum entropy approach and its application to inference problems in biology
Heliyon, 2018 A cornerstone of statistical inference, the maximum entropy framework is being increasingly applied to construct descriptive and predictive models of biological systems, especially complex biological networks, from large experimental data sets.
Andrea De Martino, Daniele De Martino
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Geometric statistical inference [PDF]
Nuclear Physics B, 1999 Finite sample size corrections to the reparametrization-invariant solution of the inverse problem of probability are computed, and shown to converge uniformly to the correct distribution.
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For Geometric Inference from Images, What Kind of Statistical Model Is Necessary? [PDF]
, 2002 In order to facilitate smooth communications with researchers in other fields including statistics, this paper investigates the meaning of "statistical methods" for geometric inference based on image feature points, We point out that statistical analysis
Kanatani, Kenichi
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Journal of the Royal Statistical Society Series B: Statistical Methodology, 1953 Summary The subject matter of mathematical statistics may be divided into two parts, the theory of probability and the theory of inference. The first is concerned with deductions from the population to the sample; the second with inferences from the sample to the population, and may further be subdivided into the design and analysis of ...
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Simultaneous statistical inference for epigenetic data. [PDF]
PLoS ONE, 2015 Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons ...
Konstantin Schildknecht +2 more
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