posterior distribution
In Bayesian statistics, the posterior distribution represents the updated beliefs about model parameters after observing data. It combines prior beliefs and the likelihood of the observed data to provide a probabilistic description of parameter values.
- A Black-Box Debiasing Framework for Conditional Sampling
- Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler
- Coupled Data and Measurement Space Dynamics for Enhanced Diffusion Posterior Sampling
- Diversifying Parallel Ergodic Search: A Signature Kernel Evolution Strategy
- Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
- Guided Diffusion Sampling on Function Spaces with Applications to PDEs
- Natural Gradient VI: Guarantees for Non-Conjugate Models
- Temporal-Difference Variational Continual Learning
- TreeGen: A Bayesian Generative Model for Hierarchies
- Variational Inference with Mixtures of Isotropic Gaussians