9780387712642 - introduction to applied bayesian statistics and estimation for social scientists (statistics for social and behavioral sciences) von lynch, scott m. (3 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2007
Serie: Buch 11 von 40 - Statistics for Social and Behavioral Sciences
- Hardcover
Anbieter: World of Books (was SecondSale), Montgomery, IL, USAWorld of Books (was SecondSale)
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Gebraucht - Befriedigend
EUR 30,00
Versand gratisVersand innerhalb von USAAnzahl: 1 verfügbar
Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
Sprache: Englisch
Verlag: Springer, 2007
Serie: Buch 11 von 40 - Statistics for Social and Behavioral Sciences
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 178,26
EUR 14,00 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Zustand: New. In.
Sprache: Englisch
Verlag: Springer Nature B.V., 2007
Serie: Buch 11 von 40 - Statistics for Social and Behavioral Sciences
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 181,89
EUR 63,73 VersandVersand von Deutschland nach USAAnzahl: 2 verfügbar
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 'Introduction to Applied Bayesian Statistics and Estimation for Social Scientists' covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feat…ure of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.
