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Found 8 papers, 0 papers with code
Bayesian Nonparametrics: An Alternative to Deep Learning
Bayesian nonparametric models offer a flexible and powerful framework for statistical model selection, enabling the adaptation of model complexity to the intricacies of diverse datasets.
A Complete Recipe for Bayesian Knowledge Transfer: Object Tracking
In this paper, we introduce a novel Bayesian model that accounts for the model-jump from which the object can choose a model and follow.
Bayesian Nonparametric View to Spawning
In tracking multiple objects, it is often assumed that each observation (measurement) is originated from one and only one object.
Bayesian Nonparametric Modelling for Model-Free Reinforcement Learning in LTE-LAA and Wi-Fi Coexistence
This work features a Nonparametric Bayesian reinforcement learning algorithm to cope with the coexistence between Wi-Fi and LTE licensed assisted access (LTE-LAA) agents in 5 GHz unlicensed spectrum.
Use of Bayesian Nonparametric methods for Estimating the Measurements in High Clutter
We robustly and accurately estimate the trajectory of the moving target in a high clutter environment with an unknown number of clutters by employing Bayesian nonparametric modeling.
Causal Adversarial Network for Learning Conditional and Interventional Distributions
The LGN is a GAN-based architecture which learns and samples from the causal model over labels.
Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking
Some challenging problems in tracking multiple objects include the time-dependent cardinality, unordered measurements and object parameter labeling.
Inference for multiple object tracking: A Bayesian nonparametric approach
In this paper, we propose robust nonparametric methods to model the state prior for MOT problem.
