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Titlebook: Selected Topics in Manufacturing; AITeM Young Research Elisabetta Ceretti,Tullio Tolio Book 2021 The Editor(s) (if applicable) and The Auth

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11#
發(fā)表于 2025-3-23 10:00:07 | 只看該作者
Filippo Montevecchi,William Hackenhaar,Gianni Campatelli
12#
發(fā)表于 2025-3-23 14:38:29 | 只看該作者
Antonios G. Stamopoulos,Alfonso Paoletti,Antoniomaria Di Ilio
13#
發(fā)表于 2025-3-23 20:07:22 | 只看該作者
ed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation..No previous experience using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems (along with comments and explanations). Notewor
14#
發(fā)表于 2025-3-23 22:40:32 | 只看該作者
Andrea Abeni,Paola Serena Ginestra,Aldo Attanasioed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation..No previous experience using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems (along with comments and explanations). Notewor
15#
發(fā)表于 2025-3-24 04:44:02 | 只看該作者
16#
發(fā)表于 2025-3-24 08:21:14 | 只看該作者
Luana Bottinitheir applications. .The primary audience for the book includes undergraduate and graduate students of science and engineering, scientific workers and engineers and specialists in the field of reliability analysis and risk assessment. Except basic knowledge of undergraduate mathematics no special pr
17#
發(fā)表于 2025-3-24 11:28:05 | 只看該作者
and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kind
18#
發(fā)表于 2025-3-24 15:49:53 | 只看該作者
19#
發(fā)表于 2025-3-24 21:01:30 | 只看該作者
20#
發(fā)表于 2025-3-24 23:49:44 | 只看該作者
Niccolò Grossi,Lorenzo Morelli,Antonio Scippan methods of Monte Carlo integration using R..Gibbs samplingThe first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous stat
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