Modeling and Estimation of Discrete-Time Reciprocal Processes via Probabilistic Graphical Models

2016/03/14 by Francesca Carli, Carli, Francesca Paola
Computer Science · Engineering · #Bayesian Modeling and Causal Inference #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #Fault Detection and Control Systems #Machine Learning (stat.ML) #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.1603.04419

openalex publication_date 2016/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Reciprocal processes are acausal generalizations of Markov processes introduced by Bernstein in 1932. In the literature, a significant amount of attention has been focused on developing dynamical models for reciprocal processes. In this paper, we provide a probabilistic graphical model for reciprocal processes. This leads to a principled solution of the smoothing problem via message passing algorithms. For the finite state space case, convergence analysis is revisited via the Hilbert metric.

Related

• Identification of Sparse Reciprocal Graphical Models
• Reciprocal Sequences as CM Sequences
• Markov-Induced CM Model
• Fundamentals of Gaussian CM Sequences
• Time-like graphical models