Variational inference: A review for statisticians
One of the core problems of modern statistics is to approximate difficult-to-compute
probability densities. This problem is especially important in Bayesian statistics, which …
probability densities. This problem is especially important in Bayesian statistics, which …
[HTML][HTML] Examining analytic practices in latent dirichlet allocation within psychological science: scoping review
Background Topic modeling approaches allow researchers to analyze and represent written
texts. One of the commonly used approaches in psychology is latent Dirichlet allocation …
texts. One of the commonly used approaches in psychology is latent Dirichlet allocation …
Laplace redux-effortless bayesian deep learning
Bayesian formulations of deep learning have been shown to have compelling theoretical
properties and offer practical functional benefits, such as improved predictive uncertainty …
properties and offer practical functional benefits, such as improved predictive uncertainty …
Machine learning and AI in marketing–Connecting computing power to human insights
Artificial intelligence (AI) agents driven by machine learning algorithms are rapidly
transforming the business world, generating heightened interest from researchers. In this …
transforming the business world, generating heightened interest from researchers. In this …
Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation
Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …
methods on various public, medical-image challenge datasets, particularly on metrics …
Marketing analytics for data-rich environments
The authors provide a critical examination of marketing analytics methods by tracing their
historical development, examining their applications to structured and unstructured data …
historical development, examining their applications to structured and unstructured data …
Black box variational inference
Variational inference has become a widely used method to approximate posteriors in
complex latent variables models. However, deriving a variational inference algorithm …
complex latent variables models. However, deriving a variational inference algorithm …
Bayesian learning for neural networks: an algorithmic survey
M Magris, A Iosifidis - Artificial Intelligence Review, 2023 - Springer
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of
the topic and the multitude of ingredients involved therein, besides the complexity of turning …
the topic and the multitude of ingredients involved therein, besides the complexity of turning …
A model of text for experimentation in the social sciences
Statistical models of text have become increasingly popular in statistics and computer
science as a method of exploring large document collections. Social scientists often want to …
science as a method of exploring large document collections. Social scientists often want to …
A survey of statistical network models
A Goldenberg, AX Zheng, SE Fienberg… - … and Trends® in …, 2010 - nowpublishers.com
Networks are ubiquitous in science and have become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …
everyday life. Formal statistical models for the analysis of network data have emerged as a …