9 papers
Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs
Ning Ning
Hidden Markov models (HMMs) are widely used probabilistic models for discrete sequential data but can be limited when hidden dynamics are complex. Hidden quantum Markov models (HQM…
Bayesian Inference for Partially Observed McKean-Vlasov SDEs with Full Distribution Dependence
Ning Ning, Amin Wu
McKean-Vlasov stochastic differential equations (MVSDEs) describe systems whose dynamics depend on both individual states and the population distribution, and they arise widely in…
Quantum Expander Mixing Lemma and its Structural Converse
Ning Ning
Expander graphs are fundamental in both computer science and mathematics, with a wide array of applications. With quantum technology reshaping our world, quantum expanders have eme…
Stationary Point Constrained Inference via Diffeomorphisms
Michael Price, Debdeep Pati, Ning Ning
Stationary points or derivative zero crossings of a regression function correspond to points where a trend reverses, making their estimation scientifically important. Existing appr…
Robust Iterative Learning Hidden Quantum Markov Models
Ning Ning
Hidden Quantum Markov Models (HQMMs) extend classical Hidden Markov Models to the quantum domain, offering a powerful probabilistic framework for modeling sequential data with quan…
Hysteretic Multivariate Bayesian Structural GARCH Model with Soft Information
Tzu-Hsin Chien, Ning Ning, Shih-Feng Huang
This study introduces the SH-MBS-GARCH model, a hysteretic multivariate Bayesian structural GARCH framework that integrates hard and soft information to capture the joint dynamics…