000			01956nam a22003017a 4500
999			_c26654 _d26654
003			arcduce
005			20220331220118.0
007			ta
008			180504s2014 txu||||| |||| 00| 0 eng d
020			_a9781597181419
040			_aarcduce _carcduce
082	0		_221 _a519.542
100	1		_99363 _aThompson, John
245	1	0	_aBayesian analysis with Stata / _cJohn Thompson.
260			_aCollege Station, Texas : _bStata Press, _c2014
300			_axx, 279 p.
504			_aBibliografía: p. 265-272.
505	0		_aPreface -- 1. The problem of priors -- 2. Evaluating the posterior -- 3. Metropolis-Hastings -- 4. Gibbs sampling -- 5. Assessing convergence -- 6. Validating the Stata code and summarizing the results -- 7. Bayesian analysis with Mata -- 8. Using WinBUGS for model fitting -- 9. Model checking -- 10. Model selection -- 11. Further case studies -- 12. Writing Stata programs for specific Bayesian analysis -- A. Standard distributions.
520	3		_aBayesian analysis with Stata is written for anyone interested in applying Bayesian methods to real data easly. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata's data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability. The book emphasizes practical data analysis from the Bayesian persepctive, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results.
541			_cDonación Prof. Fernando García.
650		4	_aANALISIS BAYESIANO _95402
650		4	_98551 _aSTATA
650		4	_aANALISIS ESTADISTICO _9138
650		4	_aESTUDIOS DE CASOS _948
650		4	_aPROGRAMAS DE COMPUTADORA _976
942			_2ddc _cLIBR _j519.542 T 55979
945			_aBEA _c2018-05-04