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 ...

We show that approximate maximum likelihood inference can be achieved via a variational algorithm for which gradient descent easily applies. We show that this setting enables us to account for ...

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 ...

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