Bibliography (35):

1. From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples

2. ​ ‘tabular ML’ directory

3. PFNs: Transformers Can Do Bayesian Inference

4. TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data

5. Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

6. Wikipedia Bibliography:

   1. Large Language Model

      https://en.wikipedia.org/wiki/Large_Language_Model

   2. Bayesian inference

   3. Stan (software)

   4. Prior predictive distribution

      https://en.wikipedia.org/wiki/Prior_predictive_distribution

   5. Markov chain Monte Carlo

   6. Bayesian hierarchical modeling

      https://en.wikipedia.org/wiki/Bayesian_hierarchical_modeling

   7. Bayes factor

   8. Leave-one-out cross-validation

      https://en.wikipedia.org/wiki/Leave-one-out_cross-validation

   9. Self-play

      https://en.wikipedia.org/wiki/Self-play

   10. GPT-3

   11. GPT-4

   12. Comma-separated values

       https://en.wikipedia.org/wiki/Comma-separated_values

   13. Just another Gibbs sampler

   14. OpenBUGS

       https://en.wikipedia.org/wiki/OpenBUGS

   15. PyMC

       https://en.wikipedia.org/wiki/PyMC

   16. Normal distribution

   17. Student's t-distribution

   18. Mixture model

   19. Change detection

       https://en.wikipedia.org/wiki/Change_detection

   20. Design of experiments

   21. Active learning

   22. Missing data imputation

       https://en.wikipedia.org/wiki/Missing_data_imputation

   23. Prediction market

   24. Leave-one-out cross-validation

       https://en.wikipedia.org/wiki/Leave-one-out_cross-validation

   25. Variance

   26. Wake-sleep algorithm

   27. Bayes factor

   28. Likelihood function § Likelihood ratio

       https://en.wikipedia.org/wiki/Likelihood_function#Likelihood_ratio

   29. Marginal likelihoods

       https://en.wikipedia.org/wiki/Marginal_likelihoods

   30. RJMCMC

       https://en.wikipedia.org/wiki/RJMCMC