De abusu linguae Graecae, in quibusdam vocibus quas Latina vsurpat, admonitio Henrici Stephani. Eiusdem Tetrasticon. ..

• Record Nr.: UNISANNIOBVEE029581
• Autore: Estienne, Henri <1528?-1598>
• Pubbl/distr/stampa: \Ginevra! : excudebat Henricus Stephanus, 1563
• Descrizione fisica: \4!, 107, \1! p. ; 8º
• Collocazione: GEA 45 0021
• Lingua di pubblicazione: Latino
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Per il luogo di stampa cfr. Adams, S1760; Marca (R 295) sul front; Cors. ; gr. ; rom; Segn.: π²a-f⁸g⁶; Fregi xil.

Mixtures : estimation and applications / / edited by Kerrie L. Mengersen, Christian P. Robert, D. Michael Titterington

• Record Nr.: UNINA9910809170003321
• Pubbl/distr/stampa: Chichester, West Sussex, : Wiley, 2011
• ISBN: 1-283-40559-8; 9786613405593; 1-119-99568-X; 1-119-99567-1
• Descrizione fisica: 1 online resource (331 p.)
• Collana: Wiley series in probability and statistics
• Altri autori (Persone): MengersenKerrie L; RobertChristian P. <1961->; TitteringtonD. M
• Disciplina: 519.2/4
• Soggetti: Mixture distributions (Probability theory); Distribution (Probability theory)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Mixtures: Estimation and Applications; Contents; Preface; Acknowledgements; List of contributors; 1 The EM algorithm, variational approximations and expectation propagation for mixtures; 1.1 Preamble; 1.2 The EM algorithm; 1.2.1 Introduction to the algorithm; 1.2.2 The E-step and the M-step for the mixing weights; 1.2.3 The M-step for mixtures of univariate Gaussian distributions; 1.2.4 M-step for mixtures of regular exponential family distributions formulated in terms of the natural parameters; 1.2.5 Application to other mixtures; 1.2.6 EM as a double expectation; 1.3 Variational approximations1.3.1 Preamble; 1.3.2 Introduction to variational approximations; 1.3.3 Application of variational Bayes to mixture problems; 1.3.4 Application to other mixture problems; 1.3.5 Recursive variational approximations; 1.3.6 Asymptotic results; 1.4 Expectation-propagation; 1.4.1 Introduction; 1.4.2 Overview of the recursive approach to be adopted; 1.4.3 Finite Gaussian mixtures with an unknown mean parameter; 1.4.4 Mixture of two known distributions; 1.4.5 Discussion; Acknowledgements; References; 2 ; Online expectation maximisation; 2.1 Introduction; 2.2 Model and assumptions2.3 The EM algorithm and the limiting EM recursion; 2.3.1 The batch EM algorithm; 2.3.2 The limiting EM recursion; 2.3.3 Limitations of batch EM for long data records; 2.4 Online expectation maximisation; 2.4.1 The algorithm; 2.4.2 Convergence properties; 2.4.3 Application to finite mixtures; 2.4.4 Use for batch maximum-likelihood estimation; 2.5 Discussion; References; 3 The limiting distribution of the EM test of the order of a finite mixture; 3.1 Introduction; 3.2 The method and theory of the EM test; 3.2.1 The definition of the EM test statistic; 3.2.2 The limiting distribution of the EM test statistic3.3 Proofs; 3.4 Discussion; References; 4 Comparing Wald and likelihood regions applied to locally identifiable mixture models; 4.1 Introduction; 4.2 Background on likelihood confidence regions; 4.2.1 Likelihood regions; 4.2.2 Profile likelihood regions; 4.2.3 Alternative methods; 4.3 Background on simulation and visualisation of the likelihood regions; 4.3.1 Modal simulation method; 4.3.2 Illustrative example; 4.4 Comparison between the likelihood regions and the Wald regions; 4.4.1 Volume/volume error of the confidence regions; 4.4.2 Differences in univariate intervals via worst case analysis4.4.3 Illustrative example (revisited); 4.5 Application to a finite mixture model; 4.5.1 Nonidentifiabilities and likelihood regions for the mixture parameters; 4.5.2 Mixture likelihood region simulation and visualisation; 4.5.3 Adequacy of using the Wald confidence region; 4.6 Data analysis; 4.7 Discussion; References; 5 Mixture of experts modelling with social science applications; 5.1 Introduction; 5.2 Motivating examples; 5.2.1 Voting blocs; 5.2.2 Social and organisational structure; 5.3 Mixture models; 5.4 Mixture of experts models
• Sommario/riassunto: This book uses the EM (expectation maximization) algorithm to simultaneously estimate the missing data and unknown parameter(s) associated with a data set. The parameters describe the component distributions of the mixture; the distributions may be continuous or discrete. The editors provide a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions along with MCMC computational methods, together with a range of detailed discussions covering the applications of the methods and features chapters from the leading experts on the subje