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Found 13 papers, 3 papers with code
Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials
We demonstrate that prognostic score adjustment in logistic regression increases the power of the Wald test for the conditional odds ratio under a fixed sample size, or alternatively reduces the necessary sample size to achieve a desired power, compared to the unadjusted analysis.
Bayesian Prognostic Covariate Adjustment With Additive Mixture Priors
We propose a new Bayesian prognostic covariate adjustment methodology, referred to as Bayesian PROCOVA, that combines these two strategies.
A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials
A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect.
Neural Boltzmann Machines
Conditional generative models are capable of using contextual information as input to create new imaginative outputs.
Can RBMs be trained with zero step contrastive divergence?
Restricted Boltzmann Machines (RBMs) are probabilistic generative models that can be trained by maximum likelihood in principle, but are usually trained by an approximate algorithm called Contrastive Divergence (CD) in practice.
Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins
Alzheimer's Disease (AD) is a neurodegenerative disease that affects subjects in a broad range of severity and is assessed in clinical trials with multiple cognitive and functional instruments.
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a complex set of clinical assessments.
Deep learning for comprehensive forecasting of Alzheimer's Disease progression
Most approaches to machine learning from electronic health data can only predict a single endpoint.
Boltzmann Encoded Adversarial Machines
Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function.
A high-bias, low-variance introduction to Machine Learning for physicists
The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists.
Bayesian feature selection with strongly-regularizing priors maps to the Ising Model
Identifying small subsets of features that are relevant for prediction and/or classification tasks is a central problem in machine learning and statistics.
Variational Pseudolikelihood for Regularized Ising Inference
I propose a variational approach to maximum pseudolikelihood inference of the Ising model.
Fast Bayesian Feature Selection for High Dimensional Linear Regression in Genomics via the Ising Approximation
Feature selection, identifying a subset of variables that are relevant for predicting a response, is an important and challenging component of many methods in statistics and machine learning.
