Advancement: A latent space approach for exchangeable multinetwork data by J. Sosa
Dissertation: A latent space approach for cognitive social structures modeling and graphical record linkage by J. Sosa
Syllabus
Summary: Applied Mathematics
Summary: Bayesian Inference
Summary: Classical Inference
Summary: Intermediate Bayesian Modeling
Summary: Linear Statistical Models
Summary: Probability
The hierarchical normal model in a linear regression setting by J. Sosa
DeGroot, M. H., & Schervish, M. J. (2012). Probability and Statistics (4th ed.). Boston: Pearson
Kadane, J. B. (2011). Principles of Uncertainty. Chapman & Hall/CRC
Probability Basics
Probability Theory Summary
Casella, G., & Berger, R. L. (2002). Statistical Inference (2nd ed.). Duxbury Press
The Solutions Manual for Statistical Inference, Second Edition
Lecture notes by D. Draper
Code by D. Draper
Fisher Information and Cramér-Rao Bound by S. Zheng
Take-home exam 1 by J. Sosa
Take-home exam 2 by J. Sosa
Take-home exam 3 by J. Sosa
Take-home exam 4 by J. Sosa
Robert, C. P. (2007). The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (2nd ed.). Springer
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014). Bayesian Data Analysis (3rd ed.). CRC Press
Hoff, P. D. (2009). A First Course in Bayesian Statistical Methods. Springer
Solutions to some exercises from BDA 3
Lecture notes by B. Sansó
Lecture notes by M. I. Jordan
Quiz 1 by J. Sosa
Quiz 2 by J. Sosa
Midterm by J. Sosa
Riley, K. F., Hobson, M. P., & Bence, S. J. (2006). Mathematical Methods for Physics and Engineering: A Comprehensive Guide (3rd ed.). Cambridge University Press
Mathematical Methods for Physics and Engineering - Solutions odds
Mathematical Methods for Physics and Engineering - Solutions evens
Prado, R., West, M., & Krystal, A. (2021). Time Series: Modeling, Computation, and Inference. Chapman & Hall/CRC Press
West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer-Verlag
Petris, G., Petrone, S., & Campagnoli, P. (2009). Dynamic Linear Models with R. Springer
Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications with R Examples (4th ed.). Springer
The Matrix Cookbook
Multivariate Normal Distribution
Lecture notes by R. Prado
Müller, P., & Rodriguez, A. (2012). Nonparametric Bayesian Inference. Lecture Notes–Monograph Series, Vol. XYZ. Institute of Mathematical Statistics, Beachwood, Ohio, USA.
Lecture Notes on Bayesian Nonparametrics by Peter Orbanz
Lecture notes by A. Rodríguez
Lecture notes by T. Kottas
Homework 1 by J. Sosa
Homework 2 by J. Sosa
Project - Variational Algorithm for Dirichlet Process Mixtures
Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). Chapman and Hall/CRC Press
Lecture notes by S. Banerjee
Homework 3 by J. Sosa
Homework 4 by J. Sosa
Homework 5 by J. Sosa
Homework 6 by J. Sosa
Christensen, R. (2011). Plane Answers to Complex Questions: The Theory of Linear Models (4th ed.). New York: Springer
Faraway, J. J. (2005). Linear Models with R. Boca Raton, FL: Chapman & Hall/CRC
Harville, D. A. (2008). Matrix Algebra from a Statistician's Perspective. New York: Springer
Some Linear Algebra
Project - Boston Data by J. Sosa
Ross, S. M. (2014). Introduction to Stochastic Processes (2nd ed.). New York: Wiley
Durrett, R. (2016). Essentials of Stochastic Processes (3rd ed.). New York: Springer
Grimmett, G., & Stirzaker, D. (2020). Probability and Random Processes (4th ed.). Oxford: Oxford University Press
Project - Hierarchical Gaussian process mixtures for regression by J. Sosa