Articulate the need for computational approaches, such as Markov chain Monte Carlo (MCMC) algorithms, to Bayesian inference. Implement various MCMC algorithms to find posterior distributions, ...

The Multi-source Probabilistic Inference (MUPI) research group studies statistical machine learning and artificial intelligence. We develop new methods and algorithms for coping with uncertainty in ...

Abstract: The fifth generation and future wireless networks are expected to support massive machine-to-machine (M2M) communications. Due to the sporadic nature, massive M2M communications can be well ...

Food System Innovations has launched an open-source Food Intelligence Lab to use AI to create better-tasting alternative ...

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Neither Sakana AI nor its external AI service providers will use customer data or inputs for model training or fine-tuning unless the client provides explicit opt-in consent.

Abstract: Sensors provide insights into the industrial processes, while misleading sensor outputs may result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian ...

Learning from potential disinformation introduces specific cognitive biases, causing individuals to systematically deviate from an idealized Bayesian updating strategy.

Baseten Inc., a startup with a platform for running artificial intelligence inference workloads, is raising $1.5 billion in ...

This important work introduces an integrated open-source platform for behavioral acquisition and pose estimation that substantially improves the accessibility and speed of real-time animal tracking ...

The bvars package includes state-of-the-art Vector Autoregressive models with Minnesota priors and a flexible structure of the error term specification. The model ...

Overview. Data transformations are a useful companion for parametric regression models. A well-chosen or learned transformation can greatly enhance the applicability of a given model, especially for ...