Lancaster EPrints
metadata only
Filtering Methods for Mixture Models .
We consider Bayesian inference for mixture distributions of known number of components via a set of filtering recursions. We extend a method - proposed in an earlier article - of direct simulation for discrete mixture distributions in order to analyze continuous mixture models. Furthermore, we intro
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- On the robustness of mixture models in the presence of hidden Markov Regimes with covariate-dependent transition probabilities#1161148
- CliPS -- How to identify cluster distributions in Bayesian mixture models#924515
- CliPS -- How to identify cluster distributions in Bayesian mixture models#920284
- Model Selection and Parameter Estimation for Multidimensional Gaussian Mixture Models with a Common Covariance Matrix#798807
- Model Selection and Parameter Estimation for Multidimensional Gaussian Mixture Models with a Common Covariance Matrix#797393
Record · ID 1039636
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive