---
title: Bayesian Modeling of Dynamic Parameters
url: https://www.ml-quant.com/papers/ssrn/4575128/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4575128
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4575128
featured: 2023-09-21
citations: unknown
topic: Econometrics & Forecasting
---


# Bayesian Modeling of Dynamic Parameters

The paper introduces a nonparametric time-varying parameter (TVP) model using Bayesian additive regression trees (BART) for macroeconomic models, providing flexibility in parameter change and easy inference.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4575128
- Identifier: SSRN 4575128
- Released: 2023-01-13
- First featured: Quant Letter No. 16 (2023-09-21): https://www.ml-quant.com/issues/2023-09-21/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [Reproducible parameter inference using bagged posteriors](https://www.ml-quant.com/papers/arxiv/2311.02019/): The BayesBag study introduces a technique of applying bagging to Bayesian posteriors to enhance reproducibility and uncertainty quantification in model misspecification.
- [Detecting toxic flow](https://www.ml-quant.com/papers/arxiv/2312.05827/): PULSE, a quick online Bayesian method, is introduced for predicting toxic trades, outperforming standard methods and offering real-time implementation.
- [A Bayesian theory of market impact](https://www.ml-quant.com/papers/arxiv/2303.08867/): The research explains how large orders, split into smaller ones (meta-orders), affect prices in financial markets, suggesting that the square-root impact law originates from the over-estimation of order flows from meta-orders.
- [TS-RSR: A Provably Efficient Approach for Batch Bayesian Optimization](https://www.ml-quant.com/papers/arxiv/2403.04764/): A novel method for batch Bayesian Optimization (BO) is introduced, which reduces redundancy and focuses on points with high predictive means or uncertainty, showing superior performance on nonconvex test functions.
- [Accelerating Convergence in Bayesian Few-Shot Classification](https://www.ml-quant.com/papers/arxiv/2405.01507/): The research combines mirror descent-based variational inference with Gaussian process for few-shot classification, enhancing accuracy, uncertainty measurement, and faster convergence.
- [Diffusive Gibbs Sampling](https://www.ml-quant.com/papers/arxiv/2402.03008/): The article introduces Diffusive Gibbs Sampling (DiGS), a new method for sampling from multi-modal distributions, which performs better in tasks like Bayesian neural networks and molecular dynamics.