Advanced Statistical Modeling Techniques

Neueste Paper

• Beyond Spectral Decomposition: Bayesian Contrastive Learning and its Non-negative Formulation via Factor Analysis

  Zhibin Duan, Tiansheng Wen, Yifei Wang, Chen Zhu, Bo Chen, Mingyuan Zhou · 30. Juni 2026

  Factor analysis, often regarded as a Bayesian variant of matrix factorization, offers superior capabilities in capturing uncertainty, modeling complex dependencies, and ensuring robustness. As the deep learning era arrives, factor analysis is receiving less and less attention due to their limited ex…

• Neural Networks as Linear Regression: An Introduction for Statisticians

  Abigail Loe, Susan Murray, Zhenke Wu · 23. Juni 2026

  Neural networks are a commonly used prediction tool in computer science and statistics. However, the barrier to entry of this interesting field remains high, particularly for classical statisticians trained in a frequentist perspective. In this letter, we demystify neural networks by describing netw…

• Measurement noise limits the advantage of nonlinear models over linear models in biomedical prediction

  Marc-Andre Schulz, Kerstin Ritter · 18. Juni 2026

  On biomedical tabular data, flexible models such as deep networks, gradient-boosted trees, and kernel methods are repeatedly matched or beaten by linear and logistic regression given the same features. The usual reaction is to treat this as a model-side shortfall, to be fixed with more data, a bette…

• Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory

  San Buchanan, Druv Pai, Peng Wang, Yi Ma · 8. Juni 2026

  In the current era of deep learning and especially generative models, there is significant investment in training very large generative models. Thus far, such models have been "black boxes" that are difficult to understand in the sense that they have opaque internal mechanisms, leading to difficulti…

• Degrees, Levels, and Profiles of Contextuality

  Ehtibar N. Dzhafarov, Victor H. Cervantes · 31. März 2026

  We introduce a new notion, that of a contextuality profile of a system. Rather than characterizing a system's contextuality by a single number, its overall degree of contextuality, we show how it can be characterized by a curve relating degree of contextuality to level at which the system is conside…

• Random Forests as Statistical Procedures: Design, Variance, and Dependence

  Nathaniel S. O'Connell · 16. Februar 2026

  Random forests are widely used prediction procedures, yet are typically described algorithmically rather than as statistical designs acting on a fixed dataset. We develop a finite-sample, design-based formulation of random forests in which each tree is an explicit randomized conditional regression f…

• Why Isn't Relational Learning Taking Over the World?

  David Poole · 6. November 2025

  Artificial intelligence seems to be taking over the world with systems that model pixels, words, and phonemes. The world is arguably made up, not of pixels, words, and phonemes but of entities (objects, things, including events) with properties and relations among them. Surely we should model these,…

• Why Isn't Relational Learning Taking Over the World?

  David Poole · 3. November 2025

  Artificial intelligence seems to be taking over the world with systems that model pixels, words, and phonemes. The world is arguably made up, not of pixels, words, and phonemes but of entities (objects, things, including events) with properties and relations among them. Surely we should model these,…