# Bayesian Methods and Mixture Models

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/bayesian-methods-and-mixture-models/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of mixture models, particularly Gaussian finite mixture models and Dirichlet process mixture models, for model-based clustering, discriminant analysis, density estimation, and unsupervised learning. It explores various inference methods such as Bayesian inference, variational inference, and Markov Chain Monte Carlo for estimating parameters in mixture models. The cluster also delves into the challenges of identifiability, variable selection, and dealing with label switching in the context of mixture models. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t11901 |
| Works | 36 |

## Topic papers all

Showing 15 of 36.

- [Regression Shrinkage and Selection Via the Lasso](https://scholariq.org/papers/regression-shrinkage-and-selection-via-the-lasso/)
- [A Co-training Approach for Multi-view Spectral Clustering](https://scholariq.org/papers/a-co-training-approach-for-multi-view-spectral-clustering/)
- [Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data](https://scholariq.org/papers/nonparametric-smoothing-estimates-of-time-varying-coefficient-models-with/)
- [Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests](https://scholariq.org/papers/bayesian-approaches-to-modeling-the-conditional-dependence-between-multiple/)
- [The BUGS Book](https://scholariq.org/papers/the-bugs-book/)
- [Variational Bayesian Inference for a Nonlinear Forward Model](https://scholariq.org/papers/variational-bayesian-inference-for-a-nonlinear-forward-model/)
- [Cubic splines to model relationships between continuous variables and outcomes: a guide for clinicians](https://scholariq.org/papers/cubic-splines-to-model-relationships-between-continuous-variables-and-outcomes-a/)
- [Quantile regression for longitudinal data using the asymmetric Laplace distribution](https://scholariq.org/papers/quantile-regression-for-longitudinal-data-using-the-asymmetric-laplace/)
- [Bayesian inference in threshold models using Gibbs sampling](https://scholariq.org/papers/bayesian-inference-in-threshold-models-using-gibbs-sampling/)
- [Linear quantile mixed models](https://scholariq.org/papers/linear-quantile-mixed-models/)
- [A Comparison of Hierarchical Methods for Clustering Functional Data](https://scholariq.org/papers/a-comparison-of-hierarchical-methods-for-clustering-functional-data/)
- [Bayesian Statistical Modelling](https://scholariq.org/papers/bayesian-statistical-modelling/)
- [Random Effects Selection in Linear Mixed Models](https://scholariq.org/papers/random-effects-selection-in-linear-mixed-models/)
- [On the performance of random‐coefficient pattern‐mixture models for non‐ignorable drop‐out](https://scholariq.org/papers/on-the-performance-of-random-coefficient-pattern-mixture-models-for-non/)
- [Statistical primer: how to deal with missing data in scientific research?†](https://scholariq.org/papers/statistical-primer-how-to-deal-with-missing-data-in-scientific-research/)

## Topic primary papers

- [Cubic splines to model relationships between continuous variables and outcomes: a guide for clinicians](https://scholariq.org/papers/cubic-splines-to-model-relationships-between-continuous-variables-and-outcomes-a/)
- [Bayesian inference in threshold models using Gibbs sampling](https://scholariq.org/papers/bayesian-inference-in-threshold-models-using-gibbs-sampling/)
- [Bayesian Statistical Modelling](https://scholariq.org/papers/bayesian-statistical-modelling/)
- [Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark](https://scholariq.org/papers/head-to-head-comparison-of-clustering-methods-for-heterogeneous-data-a/)
- [CHIME: Clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality](https://scholariq.org/papers/chime-clustering-of-high-dimensional-gaussian-mixtures-with-em-algorithm-and-its/)
- [Bayesian inference for stochastic epidemics in closed populations](https://scholariq.org/papers/bayesian-inference-for-stochastic-epidemics-in-closed-populations/)
- [James–Stein shrinkage to improve k-means cluster analysis](https://scholariq.org/papers/james-stein-shrinkage-to-improve-k-means-cluster-analysis/)
- [Statistical inference for stochastic epidemic models](https://scholariq.org/papers/statistical-inference-for-stochastic-epidemic-models/)
- [Hierarchical Normalized Completely Random Measures to Cluster Grouped Data](https://scholariq.org/papers/hierarchical-normalized-completely-random-measures-to-cluster-grouped-data/)
- [A “Density-Based” Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models](https://scholariq.org/papers/a-density-based-algorithm-for-cluster-analysis-using-species-sampling-gaussian/)
- [Inferences on the common mean of several heterogeneous log-normal distributions](https://scholariq.org/papers/inferences-on-the-common-mean-of-several-heterogeneous-log-normal-distributions/)
- [A note on testing homogeneity of the scale parameters of several inverse Gaussian distributions](https://scholariq.org/papers/a-note-on-testing-homogeneity-of-the-scale-parameters-of-several-inverse/)
- [Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge](https://scholariq.org/papers/estimating-gaussian-copulas-with-missing-data-with-and-without-expert-knowledge/)
- [Anchored Bayesian Gaussian mixture models](https://scholariq.org/papers/anchored-bayesian-gaussian-mixture-models/)
- [Estimating Gaussian Copulas with Missing Data](https://scholariq.org/papers/estimating-gaussian-copulas-with-missing-data/)

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