Learning Nonlinear Regime Transitions via Semi-Parametric State-Space Models

ArXi:2604.04963v1 Announce Type: cross We develop a semi-parametric state-space model for time-series data with latent regime transitions. Classical Marko-switching models use fixed parametric transition functions, such as logistic or probit links, which restrict flexibility when transitions depend on nonlinear and context-dependent effects. We replace this assumption with learned functions $f_0, f_1 \in \calH$, where $\calH$ is either a reproducing kernel Hilbert space or a spline approximation space, and define transition probabilities as $p_{jk,t} = \sigmoid(f(\bx_{t-1.