PGMax is an open-source Python package for easy specification of discrete Probabilistic Graphical Models (PGMs) as factor graphs, and automatic derivation of efficient and scalable loopy belief propagation (LBP) implementation in JAX. It supports general factor graphs, and can effectively leverage modern accelerators like GPUs for inference. Compared with existing alternatives, PGMax obtains higher-quality inference results with orders-of-magnitude inference speedups. PGMax additionally interacts seamlessly with the rapidly growing JAX ecosystem, opening up exciting new possibilities. Our source code, examples and documentation are available at https://github.com/vicariousinc/PGMax.

First and foremost, it is a delicious fruit. But there is a double delight for fruit-lover data scientists! It is also a Python package that implements fast and flexible probabilistic models ranging from individual probability distributions to compositional models such as Bayesian networks and Hidden Markov Models. The central idea behind this package is that all probabilistic models can be viewed as a probability distribution. That means they all yield probability estimates for samples and can be updated/fitted given samples and their associated weights.

Propel Venture Partners, BBVA's San Francisco based venture capital investment vehicle, has co-led the latest funding round into a business seeking to link up psychology and machine learning. The $5.2 million in Series A funding round into London-based AI start-up DataSine will help business launch a new platform, which combines expertise in psychology and machine learning to help smaller businesses personalize their marketing at scale. DataSine's content personalization platform Pomegranate is a collaborative AI-powered campaign platform that tailors content to personality specific to the customer. Pomegranate helps businesses tailor content to resonate with their audience, from the segment level down to the individual. It applies machine learning to behavioural data that companies already collect to build customer profiles, and provides an AI-powered content editing platform to guide marketers in tailoring a range of content elements, including words and images.

We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with---or outperform---other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code.

The Greeks shared their beliefs with the Egyptians on how to interpret good and bad dreams, and the idea of incubating dreams. In that century, other cultures influenced Greeks to develop the belief that souls left the sleeping body.