# Anchored Bayesian Gaussian mixture models

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/anchored-bayesian-gaussian-mixture-models/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Deborah Kunkel,Mario Peruggia |
| Citations | 4 |
| DOI | 10.1214/20-ejs1756 |
| Fields | Computer Science,Mathematics |
| Open Access | true |
| OA Status | gold |
| OA URL | https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-14/issue-2/Anchored-Bayesian-Gaussian-mixture-models/10.1214/20-EJS1756.pdf |
| OpenAlex ID | https://openalex.org/W2803399520 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Deborah Kunkel](https://scholariq.org/researchers/deborah-kunkel/)

## Paper primary topic

- [Bayesian Methods and Mixture Models](https://scholariq.org/topics/bayesian-methods-and-mixture-models/)

## Paper topics

- [Bayesian Methods and Mixture Models](https://scholariq.org/topics/bayesian-methods-and-mixture-models/)
- [Gaussian Processes and Bayesian Inference](https://scholariq.org/topics/gaussian-processes-and-bayesian-inference/)
- [Statistical Methods and Bayesian Inference](https://scholariq.org/topics/statistical-methods-and-bayesian-inference/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.