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STA 250 Lectures
- Notetaker 1
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- Aden: md
- Izyumin: tex, pdf
- Werner: tex, pdf
- Rohosen: tex, pdf
- Meisner: tex, pdf
- Cai: tex, pdf
- Fan: tex, pdf
- Shvarts: tex, pdf
- Wang: tex, pdf
- Chen: tex, pdf
- Cheung: tex, pdf
- Shuo: tex, pdf
- Ganesh: tex, pdf
- Bissell: tex, pdf
- Chan: tex, pdf
- Aden: md/tex, pdf
- Sonmez: tex, pdf
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- Notetaker 2
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- Dai: tex, pdf
- Huang: tex, pdf
- Gao: tex, pdf
- Ulle: tex, pdf
- Fan: tex, pdf
- Hsueh: tex, pdf
- Ji: tex, pdf
- (None)
- Nguyen: tex, pdf
- Pei-Chen: tex, pdf
- Paisley: tex, pdf
- Shuyang: tex, pdf
- Tao: tex, pdf
- Li: tex, pdf
- Shev: tex, pdf
- Shev: tex, pdf
- Qin: tex, pdf
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- Notetaker 3
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- Liu: tex, pdf
- Wang: tex, pdf
- Filshtein: tex, pdf
- (None)
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- Lee: tex, pdf
- Chen: tex, pdf
- (None)
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- Li: tex, pdf
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- Kuo: tex, pdf
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References
Lecture 04 References (Bayes Lecture 1):
- Bernardo, J.M. (1979) Reference Posterior Distributions for Bayesian Inference. JRSSB, 41, 2, pp. 113-147
- Welch, B.L. and Peers, H.W. (1963) On formulae for confidence points based on integrals of weighted likelihoods. JRRSB, 25-2, pp. 318-329
- Goldstein, M. (2006) Subjective Bayesian Analysis: Principles and Practice. Bayesian Analysis, 1-3, pp. 403-420.
Lecture 05 References (Bayes Lecture 2):
Lecture 06 References (Bayes Lecture 3):
- Cook, S.R., Gelman, A., Rubin, D.B. (2006) Validation of Software for Bayesian Models Using Posterior Quantiles. Journal of Computational and Graphical Statistics, 15, 3, 675-692.
- Chib, S., Greenberg, E. (1995) Understanding the Metropolis-Hastings Algorithm. The American Statistician, 49, 4, 327-335.
Lecture 07 References (Bayes Lecture 4):
Lecture 08 References (Big Data Lecture 1):
- Efron, B. (1979) Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics. 7, 1, 1-26.
- Kleiner, A., Talwalkar, A., Sarkar, P., Jordan, M.I. (2011) A Scalable Bootstrap for Massive Data. arXiv: 1112.5016
Lecture 09 References (Big Data Lecture 2):
Lecture 10 References (Big Data Lecture 3):
Lecture 11 References (Big Data Lecture 4):
Lecture 12 References (Optimization + EM Lecture 1):
Lecture 13 References (Optimization + EM Lecture 2):
Lecture 14 References (Optimization + EM Lecture 3):
Lecture 15 References (Optimization + EM Lecture 4):
Lecture 16 References (Parallelization + GPUs Lecture 1):
Lecture 17 References (Parallelization + GPUs Lecture 2):
Lecture 18 References (Parallelization + GPUs Lecture 3):
Happy coding! :)