Automating inference for non–standard models
<p>Probabilistic models enable us to infer the underlying relationships within data and make decisions based on this information. Certain models are more commonly used not because they are more appropriate to imitate a particular system, but because they are simple enough to analyze given the...
主要作者: | Zhou, Y |
---|---|
其他作者: | Rainforth, T |
格式: | Thesis |
语言: | English |
出版: |
2020
|
主题: |
相似书籍
-
Automating inference, learning, and design using probabilistic programming
由: Rainforth, T
出版: (2017) -
Static posterior inference of Bayesian probabilistic programming via polynomial solving
由: Wang, Peixin, et al.
出版: (2024) -
Neural networks for inference, inference for neural networks
由: Webb, S
出版: (2018) -
Towards formal verification of Bayesian inference in probabilistic programming via guaranteed bounds
由: Zaiser, F
出版: (2024) -
Inference on Markov random fields: methods and applications
由: Lienart, T
出版: (2017)