Issue #187
December 26, 2022
This week’s Data Science Book is " Computer Age Statistical Inference " by B. Effron and T. Hastie. This book provides a comprehensive overview of modern statistical methodology, covering a wide range of topics including Bayesian and frequentist approaches, survival analysis, logistic regression, empirical Bayes, random forests, neural networks, Markov chain Monte Carlo, and model selection. The authors are two well-known experts from Stanford University that are able to discuss the history of statistical analysis and its evolution with the introduction of electronic computation in the 1950s and offer a modern approach that integrates methodology and algorithms with statistical inference. This book is a valuable resource for understanding the flow of statistical thinking and for choosing the best approach to solve data analysis problems. It is well-written and well-produced, with good examples and a small amount of *gasp* R code.
- 1. Introduction to Locality-Sensitive Hashing [tylerneylon.com]
- 2. The Mathematical Hacker [evanmiller.org]
- 3. SQLite Internals: How The World's Most Used Database Works [compileralchemy.com]
- 4. Characterizing Emergent Phenomena in Large Language Models [ai.googleblog.com]
- 5. Boring Python: code quality [b-list.org]
- 6. AI: Science Fiction vs Reality [kozyrkov.medium.com]
- 7. 8 Tips for Creating Data Visualizations in Python using Bokeh [towardsdatascience.com]
- • Aging is associated with a systemic length-associated transcriptome imbalance (T. Stoeger, R. A. Grant, A. C. McQuattie-Pimentel, K. R. Anekalla, S. S. Liu, H. Tejedor-Navarro, B. D. Singer, H. Abdala-Valencia et al)
- • Seasonality, density dependence, and spatial population synchrony (P. G. Nicolau, R. A. Ims, S. H. Sørbye, N. G. Yoccoz)
- • The role of different “media diets” on the perception of immigration: Evidence from nine European countries (L. Terren)
- • Multidimensional Tie Strength and Economic Development (L. M. Aiello, S. Joglekar, D. Quercia)
- • Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (C. Rudin, C. Chen, Z. Chen, H. Huang, L. Semenova, C. Zhong)
- • Overlapping community detection on complex networks with Graph Convolutional Networks (S. Yuan, H. Zeng, Z. Zuo, C.Wang)
- • A Retrieve-and-Read Framework for Knowledge Graph Link Prediction (V. Pahuja, B. Wang, H. Latapie, J. Srinivasa, Y. Su)
Twintrees, Baxter Permutations, and Floorplans
All our videos are also available in our YouTube playlist.
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