Uygulamalı Bayesçi İstatistik
Özet
Referanslar
Baker, C. L., Saxe, R. R., & Tenenbaum, J. B., 2009, Action understanding as inverse planning, Cognition, 113(3), 329–349.
Berger, J.O., 1985, Statistical Decision Theory and Bayesian Analysis, Springer Verlag, New York.
Berger, J. O., and Bernardo, J. M., 1992, On the development of reference priors (with discussion). In J. M. Bernardo, J. O. Berger, A. P. Dawid, & A. F. M. Smith (Eds.), Bayesian Statistics 4 (pp. 35–60). Oxford University Press.
Bernardo, J.M., 1979, Reference posterior distributions for bayesian inference, The J. of the Royal Stat. Soc., B, 41, 2, 113-147.
Bishop, C. M., 2006, Pattern Recognition and Machine Learning. New York: Springer.
Box, G.E.P and Tiao, G.C., 1973, Bayesian Inference in Statistical Analysis, Addison-Wesley
Carlin, B.P. and Chib, S., 1995, Bayesian model choice via markov chain monte carlo methods, J.R. Statist. Soc., B, 57, No. 3: 473-484.
Carlin, B.P. and Louis, T.A., 1997, Bayes and Empirical Bayes Methods for Data Analysis, Chapman and Hall, London.
Cavanaugh, J.E., 1997, Unifying the derivations for the Akaike and corrected Akaike information criteria, Statistics and Probability Letters, Vol 33, 201-208.
Chalton, D.O., and Troskie, C.G., 1993, On the compatibility of sample and prior information in the mixed regression model, Commun.Statist.-Theory Meth., 22(3), 921-928.
Clark, A., 2016, Surfing Uncertainty: Prediction, Action and the Bayesian Brain. Oxford University Press.
Clyde, Merlise A., 1999, Bayesian model averaging and model search strategies, Bayesian Statistics 6, 157-185.
De Groot, M.H., 1970, Optimal Statistical Decision, McGraw-Hill Inc, New York.
Draper, N. and Smith, H., 1981, Applied Regression Analysis, John Wiley.
Erar, A., 1982, Çoklubağlantı varlığında doğrusal regresyon modellerinde değişken seçimi: Doktora Tezi (yayınlanmamış), H.Ü. Fen Fakültesi, Beytepe, Ankara, 120s.
Fernandez, C., Ley, E., and Steel, M. F., 2001, Benchmark priors for bayesian model averaging, Journal of Econometrics, 100, 381–427.
Foster, P. and George E. I., 1994, The risk inflation criterion for multiple regression, The Annals of Statistics, 22, 1947-1975.
Frankish, K. and Ramsey, W. M., 2021, The Cambridge Handbook of Cognitive Science, Cambridge University Press.
Gamerman, D., 1997, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Chapman and Hall, London, 245p.
Garthwaite, P. H., Kadane, J. B., and O'Hagan, A., 2005, Statistical methods for eliciting probability distributions, Journal of the American Statistical Association, 100(470), 680-701.
Gazzaniga, M. S., Ivry, R. B., ve Mangun, G. R., 2018, Cognitive Neuroscience: The Biology of the Mind (5th ed.), W. W. Norton & Company.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. and Rubin, D. B., 2013, Bayesian Data Analysis (3rd ed.), Chapman & Hall/CRC.
George, E. I., and McCulloch, R. E.,1993, Variable selection via gibbs sampling, Journal of the American Statistical Association, 88(423), 881–889.
George, E. I. and McCulloch, R. E., 1997, Approaches for bayesian variable selection, Statistica Sinica, 7(2), 339–373.
Gershman, S. J. and Beck, J. M., 2021, Bayesian models of perception and action. In Computational Psychiatry (pp. 3–26). MIT Press
Ghosh, J.K., Delampady, M., Samanta, T., An introduction to Bayesian analysis: theory and methods, 2006, Springer Science+Business Media.
Goldstein, E. B., 2019, Cognitive Psychology: Connecting Mind, Research, and Everyday Experience (5th ed.), Cengage Learning.
Goldstein, M.,1976, Bayesian analysis of regression problems: Biometrika, 63, 1, 51-58.
Goodman, S. N., 1999, Toward evidence-based medical statistics. 2: The Bayes factor, Annals of Internal Medicine, 130(12), 1005–1013.
Griffiths, T. L., Lieder, F. and Goodman, N. D., 2015, Rational use of cognitive resources: Levels of analysis between the computational and the algorithmi, Topics in Cognitive Science, 7(2), 217–229.
Gunst, R.F. and Mason, R.L., 1977, Biased estimation in regression: an evaluation using mean squared error, JASA, 72, 616-627.
Hamburg, M.,1974, Statistical Analysis for Decision Making, Harcourt Brace Jovanovich Inc.
Hasan, Iftekhar, Horvath Roman, Mares Jan., 2016, What type of finance matters for growth?: Bayesian model averaging evidence, Policy Research Working Paper; No. 7645. World Bank, Washington, DC.
Hasting, W.K., 1970, Monte carlo sampling methods using markov chains and their applications, Biometrika, 57, 97-109.
Held, L. and Ott, M., 2016, How the maximal evidence of p-values against point null hypotheses depends on sample size, The American Statistician, 70(4), 335–341.
Hemmer, P., & Steyvers, M., 2009, A Bayesian account of reconstructive memory, Topics in Cognitive Science, 1(1), 189–202.
Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T., 1999, Bayesian model averaging: A tutorial., Statistical Science, 14(4), 382–417.
Ishwaran, H. and Rao, J. S., 2005, Spike and slab variable selection: Frequentist and Bayesian strategies, Annals of Statistics, 33(2), 730–773.
Jeffreys H., 1961, Theory of Probability, Oxford, UK: Oxford Univ. Press. 3rd ed.
Jeffrey, R., 2004, Subjective probability: The real thing, Cambridge University Press.
Jensen, F. V. and Nielsen, T. D., 2007, Bayesian Networks and Decision Graphs (2nd ed.). Springer.
Kaplan, D., and Depaoli, S., 2013, Bayesian statistical methods., In T. D. Little (Ed.), Oxford handbook of quantitative methods: Vol. 2. Statistical analysis (pp. 407–437). New York: Oxford University.
Kass, R. and Wasserman L., 1995, A reference bayesian test for nested hypotheses and its relationship to the schwarz criterion, Journal of the American Statistical Association, 90 (431),928-934.
Kass, R.E. and Raftery, A.E., 1995, Bayes factors, JASA, 90, 773-795.
Knill, D. C. and Pouget, A., 2004, The Bayesian brain: The role of uncertainty in neural coding and computation, Trends in Neurosciences, 27(12), 712–719.
Koller, D. and Friedman, N., 2009, Probabilistic Graphical Models: Principles and Techniques, MIT Press.
Köklü, M., Sarıgil, S., and Özbek, O., 2021, The use of machine learning methods in classification of pumpkin seeds (Cucurbita pepo L.), Genetic Resources and Crop Evolution, 68(7), 2713-2726.
Lee, M. P., 1989, Bayesian Statistics: An introduction, Oxford University Press.
Liang, F., Paulo, R., Molina, G., Clyde, M. A., and Berger, J. O., 2008, Mixtures of g priors for Bayesian variable selection, Journal of the American Statistical Association, 103(481), 410-423.
Lieder, F., and Griffiths, T. L., 2020, Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources, Behavioral and Brain Sciences, 43, E1.
Lindley, D. V., 1965, Introduction to Probability and Statistics from a Bayesian Viewpoint (Vols. I & II). Cambridge University Press.
Lindley, D.V. and Smith, A.F.M., 1972, Bayes estimates for the linear model, J.R. Statist. Soc., 1-41.
Liu, Y., and Abeyratne, A. I., 2019, Practical Applications of Bayesian Reliability, John Wiley & Sons.
Liu, Y., Quan, Q., Abeyratne, A. I., Zhou, M., Jiang, Y., and Sun, J., 2021, Bridging human and artificial intelligence: Insights from cognitive science, Trends in Cognitive Sciences, 25(10), 856–868.
Ma, W. J., and Jazayeri, M., 2022, Neural computations underlying probabilistic inference in the primate brain, Annual Review of Neuroscience, 45, 1-22.
Mallows, C. L., 1973, Some comments on Cp, Technometrics, 15(4), 661–675.
Manning, C. D., Raghavan, P., Schütze, H., 2008, Introduction to Information Retrieval, Cambridge: Cambridge University Press.
Matlin, M. W., 2020, Cognition (10th ed.). Wiley.
Miller, A.J., 1984, Selection of subsets of regression variables, J.R. Statist. Soc., A 147, part 3: 389-425.
Mitchell, T. M., 2021, Machine Learning (Updated ed.), McGraw-Hill. (Original edition: 1997; updated content released in 2021).
Mitchell, T.J. and Beauchamp, J.J., 1988, Bayesian variable selection in linear regression, JASA, Vol. 83, No. 404, 1023-1036.
Montgomery, D. C., 2013, Design and Analysis of Experiments (8th ed.). John Wiley & Sons
Montgomery, D.C. and Peck,E.A., 1982, Introduction to Linear Regression Analysis, Wiley.
Moulton, B. I., 1991, A bayesian approach to regression selection and estimation, with application to price index for radio services, Journal of Econometrics 49, 169–193.
Myers, R. H., 1986, Classical and Modern Regression with Applications, Duxbury Press.
O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., and Rakow, T., 2006, Uncertain Judgements: Eliciting Experts’ probabilities, John Wiley & Sons.
Pearl, J., 1988, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
Pilz, J., 1991, Bayesian Estimation and Experimental Design in Linear Regression Models, Chichester: John Wiley & Sons.
Pouget, A., Beck, J. M., Ma, W. J., Latham, P. E., 2013, Probabilistic brains: Knowns and unknowns. Nature Neuroscience, 16(9), 1170–1178.
Press, S.J., 1989, Bayesian Statistics: Principles, Models and Applications, John Wiley Inc.
Russell, S. J., and Norvig, P., 2020, Artificial Intelligence: A Modern Approach, Pearson.
Schwarz, G., 1978, Estimating the dimension of a model, Annals of Statistics, 6(2), 461-464.
Tversky, A., & Kahneman, D., 1974, Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
van der Vaart, A. W., 1998, Asymptotic Statistics, Cambridge University Press.
Viallefont, V., Raftery, A. E., Richardson S., 2001, Variable selection and Bayesian model averaging in case-control studies, Statistics In Medicine, 20, 3215-3230.
Vinod, H. D., and Ullah, A., 1981, Recent Advances in Regression Methods, New York: Marcel Dekker.
Wasserman, L., 1997, Bayesian model selection, Symposium on Methods for Model Selection, Indiana Uni., Bloomington.
Weisberg, S., 1980, Applied Linear Regression, Wiley Series in Probability and Mathematical Statistics, New York: John Wiley & Sons.
Winkler, R.L., 1972, Introduction to Bayesian Inference and Decision, Holt, Rinehart and Winston Inc.
Xu, F., and Kushnir, T., 2020, Probabilistic learning in infancy In L. S. Liben & J. M. Call (Eds.), Oxford Research Encyclopedia of Psychology. Oxford University Press.
Yardımcı, A. ve Erar, A., 1991, Çoklubağlantılı doğrusal regresyonda Bayes yaklaşımı, Hacettepe Fen ve Mühendislik Bilimleri Dergisi, Cilt 12, Seri B, 53-68.
Yardımcı, A., 1992, Çoklubağlantılı çoklu doğrusal regresyonda Bayes yaklaşımı: Bilim uzmanlığı tezi, (yayınlanmamış), H.Ü. Fen Fakültesi, Beytepe, Ankara.
Yardımcı, A, ve Erar, A., 1995, Bazı yanlı kestirim yöntemleri üzerine bir benzetim çalışması, Hacettepe Fen ve Mühendislik Bilimleri Dergisi, 16, 215-231.
Yardımcı, A., 2000, Doğrusal regresyonda değişken seçimine Bayes yaklaşımlarının karşılaştırılması, Doktora Tezi, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
Yardımcı, A and Erar, A., 2002, Bayesian variable selection in linear regression an a comparison, Hacettepe Journal of Mathematics and Statistics, 31, 63-76 (2002).
Yardımcı, A. ve Erar, A., 2010, Aykırı değer varlığında doğrusal regresyonda değişken seçimine gibbs örneklemesi yaklaşımı. Gazi University Journal of Science, 18(4), 603-611.
Yardımcı, A., 2019, Bayesci model ortalaması yöntemi: istihdam oranı üzerine bir uygulama, İstatistikçiler Dergisi: İstatistik & Aktüerya, 1, 15-31.
Yücel, Z.T., 1987, Çoklu doğrusal regresyonda değişken seçimi için Cp ve PRESSp ölçütlerinin kullanımları üzerine bir çalışma: Bilim uzmanlığı tezi (yayınlanmamış), H.Ü. Fen Fakültesi, Beytepe, Ankara.
Zellner, A., 1971, An Introduction to Bayesian Inference in Econometrics, Wiley Series in Probability and Mathematical Statistics, New York: John Wiley & Sons.
Zellner, A. and Siow, A., 1980, Posterior odds ratio for selected regression hypothesis, Bayesian Statistics, University Press, Valencia, 585–603.
Zellner, A., 1985, Bayesian econometrics, Econometrica, 53(2), 253–269.
Zellner, A., 1986, On assessing prior distributions and bayesian regression analysis with gprior distributions, Bayesian inference and decision techniques: Essays in Honor of Bruno De Finetti, 6,233–243.
İndir
Yayınlanan
Lisans
LisansBu İnternet Sitesi içeriğinde yer alan tüm eserler (yazı, resim, görüntü, fotoğraf, video, müzik vb.) Akademisyen Kitabevine ait olup, 5846 sayılı Fikir ve Sanat Eserleri Kanunu ve 5237 sayılı Türk Ceca Kanunu kapsamında korunmaktadır. Bu hakları ihlal eden kişiler, 5846 sayılı Fikir ve Sanat eserleri Kanunu ve 5237 sayılı Türk Ceza Kanununda yer alan hukuki ve cezai yaptırımlara tabi olurlar. Yayınevi ilgili yasal yollara başvurma hakkına sahiptir.