1 citations · 1 across the 8 of their papers we have counts for
8 papers
Continuous Hidden Markov Models for Equity Returns: Heavy-Tail Emission Families and Regime-Conditional Value-at-Risk
Abdulrahman Alswaidan, Cade Jin, Jeffrey D. Varner
Synthetic generators of daily equity returns let practitioners stress test, backtest, and design scenarios that a single realized market history cannot supply, but only if the gene…
Variance-Corrected Multi-Asset Equity Simulation with Hybrid Hidden Markov Marginals
Abdulrahman Alswaidan, Jeffrey D. Varner
Synthetic multi-asset equity data must reproduce each asset's return distribution and its relationship with the market. Reusing a generator fitted to full asset returns creates a p…
Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy
Jeffrey D. Varner, Maria Cristina Bravo, Carole McBride +2
Small longitudinal cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling because enrollment is slow and the data are too sparse to…
Conditioning Protein Generation via Hopfield Pattern Multiplicity
Jeffrey D. Varner
Small protein-family alignments often contain a subset of interest but not enough labeled data to train a conditional generator. We condition a training-free stochastic-attention s…
Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention
Jeffrey D. Varner
Generating novel protein sequences that respect a family's statistical constraints typically requires training deep generative models on thousands to millions of examples. Yet most…
Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy
Abdulrahman Alswaidan, Jeffrey D. Varner
Attention heads retrieve: given a query, they return a weighted average of stored values. We showed that this computation is one step of gradient descent on the modern Hopfield ene…
MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series
Aaron Wheeler, Jeffrey D. Varner
This work presents a generative pre-trained transformer (GPT) designed for modeling financial time series. The GPT functions as an order generation engine within a discrete event s…
Scalable Agent-Based Modeling for Complex Financial Market Simulations
Aaron Wheeler, Jeffrey D. Varner
In this study, we developed a computational framework for simulating large-scale agent-based financial markets. Our platform supports trading multiple simultaneous assets and lever…