Probabilistic Programming and Inference Algorithms Publication Trend The graph below shows the total number of publications each year in Probabilistic Programming and Inference Algorithms.

Latent space models are popular for analyzing dynamic network data. We propose a variational approach to estimate the model parameters and the latent positions of the nodes in the network. The ...

The resultant algorithms have ready-to-implement closed form expressions and allow a broad class of arbitrarily large semiparametric regression models to be handled. Ongoing software projects such as ...

Yoshihiro Tawada proposes using variational inference – a technique widely used in machine learning – to obtain foreign exchange implied volatilities with nonlinear constraints for strike-order ...

Teaching-related activities comprise approximately 5% of the workload. The ideal candidate has a strong background in computational statistics and/or machine learning, particularly in approximate ...

MicroAlgo's classifier auto-optimization technology, based on variational quantum algorithms, successfully reduces the computational complexity of parameter updates through deep optimization of ...