Search Results for "bayesian"

...The platform is designed for performance and scale: its SDKs are lightweight, supporting local evaluation to minimize latency, and it integrates deeply with existing data stacks so you can use your warehouse or analytics system as the source of truth. Experimentation in GrowthBook isn’t just toggles; its statistics engine supports advanced techniques like CUPED, Bayesian, and sequential testing, and control group checks so you can confidently measure impact.

BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes.

JAGS is Just Another Gibbs Sampler. It is a program for the statistical analysis of Bayesian hierarchical models by Markov Chain Monte Carlo.

...It provides updated notebooks, R scripts, and model examples, some streamlined and restructured compared to previous years. The 2024 repo also highlights the transition toward more robust Stan models and integration with newer Bayesian workflow practices, continuing to emphasize accessibility for learners while modernizing the tools. This version is designed for students following the 2024 lecture series, offering the most current set of examples, exercises, and teaching material aligned with the Statistical Rethinking framework. Online, flipped instruction. I will pre-record the lectures each week. ...

Neural Tangents is a high-level neural network API for specifying complex, hierarchical models at both finite and infinite width, built in Python on top of JAX and XLA. It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels,...

...It continues to provide scripts for lectures and tutorials, while integrating refinements to examples, notation, and computational workflows introduced that year. Compared with 2022, some models are rewritten for clarity, and teaching materials reflect refinements in McElreath’s evolving presentation of Bayesian data analysis. Students following the 2023 lecture videos use this repository as their coding reference. There are 10 weeks of instruction. Links to lecture recordings will appear in this table. Weekly problem sets are assigned on Fridays and due the next Friday, when we discuss the solutions in the weekly online meeting.

This repository hosts the 2022 version of the Statistical Rethinking course. It contains course materials such as R scripts, notebooks, and worked examples aligned with McElreath’s textbook. The code emphasizes Bayesian data analysis using R, the rethinking package, and Stan models. It includes lecture code files, example datasets, and structured exercises that parallel the topics covered in the lectures (probability, regression, model comparison, Bayesian updating). The repo functions as a direct hands-on reference for students following the 2022 recorded lecture series. ...

...They can learn distributions over functions from data and efficiently make predictions at new inputs with calibrated uncertainty — making them useful for few-shot learning, Bayesian regression, and meta-learning. Each notebook includes theoretical explanations, key building blocks, and executable code that runs directly in Google Colab, requiring no local setup. Implementations rely only on standard dependencies such as NumPy, TensorFlow, and Matplotlib, and provide visualizations of model performance.

...You paint with the mouse into its dreams and it responds by changing what you painted gradually. There will also be an API for using it with other programs as a general high-dimensional space. Each pixel's brightness is its own dimension. Bayesian nodes have exactly 3 childs because that is all thats needed to do NAND in a fuzzy way as Bayes' Rule which is NAND at certain extremes. NAND can be used to create any logical system. In this early version, I'm still working on edge detection and its understanding of the same shapes at different brightnesses. This will be a module of the bigger Human AI Net project and will be used for adding realtime intuitive high dimensional intelligence in audio and visual interactions with the user.

Provides a set of tools for processing text, such as text extraction and classification. Classification implementations to be implemented include: Bayesian and Statistical (N-gram).

An implementation of memory-prediction framework applied for image recognition. Based on Jeff Hawkins' book On Intelligence. It models the high-level hierarchical architecture of human neocortex and uses Bayesian belief revision for making predictions

A C++ library for Bayesian computation, including a collection of more generally-applicable utilities.