Valid Decoding in Gaussian Mixture Models

• Published in: 2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS) pp. 1 - 6
• Main Authors: Rudic, Branislav, Pichler-Scheder, Markus, Efrosinin, Dmitry
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.08.2024
• Subjects: Data models; Decoding; Dynamic Systems; Filtering; Filtering algorithms; Gaussian mixture model; Gaussian Mixtures; Hidden Markov Models; Inference algorithms; Information technology; MAP Inference; Noise; Object tracking; Prediction algorithms; Simulation; Smoothing; State Observation Models
• DOI: 10.1109/CITDS62610.2024.10791365

A novel recursive Bayesian inference method for state observation models with Gaussian mixture assumptions is presented. The proposed approach is located between marginal and maximum a posteriori (MAP) inference, both of which have been extensively explored over the last decades. A tight coupling is revealed between inferring the predicted and filtered marginal distributions and recursively decoding MAP predecessors. Based on these findings, an algorithm is presented to decode state sequences that are valid, i.e., consistent with underlying model assumptions. Since Gaussian mixtures can be used as universal approximators for density functions, an appropriate decoder holds considerable potential for various applications. Preliminary simulation results from ongoing research on object tracking, where observations are affected by multimodal noise, suggest that the proposed decoder may exhibit superior characteristics over traditional inference methods.