Soft Bayesian Context Tree Models for Real-Valued Time Series
Not on the map. Why?
Abstract (arXiv)
This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers soft (probabilistic) splits of the context space, instead of hard (deterministic) splits of the context space as in the previous BCT for real-valued time series. A learning algorithm of the Soft-BCT is proposed based on the variational inference. The results of experiments demonstrate the superiority of the Soft-BCT compared to the previous BCT for some datasets.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 47 references that are in the map. First posted on arXiv 16 Jan 2026.
Builds on
8 of 47 references.
References · 8
- 2013Streaming Variational Bayescited by 46
- 2020Bayesian Context Trees: Modelling and exact inference for discrete time seriescited by 12
- 2021Probability Distribution on Full Rooted Treescited by 9
- 2022Posterior Representations for Bayesian Context Trees: Sampling, Estimation and Convergencecited by 6
- 2022Truly Bayesian Entropy Estimationcited by 3
- 2023Batch Updating of a Posterior Tree Distribution over a Meta-Treecited by 3
- 2023The Bayesian Context Trees State Space Model for time series modelling and forecastingcited by 2
- 2021Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture modelscited by 1
Built on by
Citing papers per year: 2026: 2.
2 papers.