Implementing MCMC sampling from scratch in R for various Bayesian models
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Updated
Dec 7, 2023 - HTML
Implementing MCMC sampling from scratch in R for various Bayesian models
Markov Chain Monte Carlo (MCMC) and importance sampling in the context of Bayesian linear regression
NTHU EE6550 Machine Learning Course Projects (include Maximum A Posteriori Estimation, Linear Regression, Neural Network Image Classification)
End-to-end Bayesian linear regression pipeline: conjugate baselines + non-conjugate priors, adaptive Metropolis-within-Gibbs, and rigorous diagnostics (R-hat/ESS/MCSE) with posterior predictive checks for salary modeling.
Bigmart Sales Analysis prediction of the sales, data set from https://datahack.analyticsvidhya.com/contest/practice-problem-big-mart-sales-iii/
Statistical inference, hypothesis testing, ANOVA, correlation, and regression across datasets, including checks of statistical assumptions.
Coresets for scalable robust pseudo-Bayesian inference
Bayesian Linear regression using MCMC and Variational Inference
Sklearn wrapper for Bayesian Probabilistic Matrix Completion with Macau, Smurff, and Bayesian linear regression
Summer Research Internship Tasks
Ensembled Deep Network for Global Optimization
Entraînement de modèles Bayésiens et analyse de robustesse.
Artificial Intelligence -> Machine Learning (ML) -> Supervised Learning -> Regression -> Bayesian Linear Regression, Bayesian Ridge Regression, Empirical Bayes Regression
Bayesian linear and Gaussian process regression to predict CO2 concentration as a function of time
Bayesian uncertainty, calibration, and hallucination-risk modeling for reliable language and multimodal AI.
🤖 Explore and optimize rewards with Bandexa, a PyTorch-native library for Neural-Linear Thompson Sampling in contextual bandits.
code for performing Bayesian ARD regression, where covariates have groups
Mini-course for Elizabeth Liu
Machine Learning Projects on Linear Regression, Classification and Multi-layer Perceptrons, and Gaussian Processes and SVMs (October, 2019).
My implementation of homework 2 for the Machine Learning class in NCTU (course number 5088).
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