Variational inference: A review for statisticians

DM Blei, A Kucukelbir, JD McAuliffe - Journal of the American …, 2017 - Taylor & Francis
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 …

[HTML][HTML] Examining analytic practices in latent dirichlet allocation within psychological science: scoping review

LJ Hagg, SS Merkouris, GA O'Dea, LM Francis… - Journal of Medical …, 2022 - jmir.org
Background Topic modeling approaches allow researchers to analyze and represent written
texts. One of the commonly used approaches in psychology is latent Dirichlet allocation …

Laplace redux-effortless bayesian deep learning

E Daxberger, A Kristiadi, A Immer… - Advances in …, 2021 - proceedings.neurips.cc
Bayesian formulations of deep learning have been shown to have compelling theoretical
properties and offer practical functional benefits, such as improved predictive uncertainty …

Machine learning and AI in marketing–Connecting computing power to human insights

L Ma, B Sun - International Journal of Research in Marketing, 2020 - Elsevier
Artificial intelligence (AI) agents driven by machine learning algorithms are rapidly
transforming the business world, generating heightened interest from researchers. In this …

Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation

T Nair, D Precup, DL Arnold, T Arbel - Medical image analysis, 2020 - Elsevier
Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …

Marketing analytics for data-rich environments

M Wedel, PK Kannan - Journal of marketing, 2016 - journals.sagepub.com
The authors provide a critical examination of marketing analytics methods by tracing their
historical development, examining their applications to structured and unstructured data …

Black box variational inference

R Ranganath, S Gerrish, D Blei - Artificial intelligence and …, 2014 - proceedings.mlr.press
Variational inference has become a widely used method to approximate posteriors in
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 …

A model of text for experimentation in the social sciences

ME Roberts, BM Stewart, EM Airoldi - Journal of the American …, 2016 - Taylor & Francis
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 …

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 …