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Bayesian Inference

244 papers with code · Methodology

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

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Latest papers with code

Bayesian Context Aggregation for Neural Processes

ICLR 2021 iclr2021637/iclr2021637

Recently, casting probabilistic regression as a multi-task learning problem in terms of conditional latent variable (CLV) models such as the Neural Process (NP) has shown promising results.

BAYESIAN INFERENCE MULTI-TASK LEARNING

0
01 Jan 2021

Baxter Permutation Process

NeurIPS 2020 nttcslab/baxter-permutation-process

Compared with conventional BNP models for arbitrary RPs, the proposed model is simpler and has a high affinity with Bayesian inference.

BAYESIAN INFERENCE

0
01 Dec 2020

Bayesian Pseudocoresets

NeurIPS 2020 trevorcampbell/pseudocoresets-experiments

Standard Bayesian inference algorithms are prohibitively expensive in the regime of modern large-scale data.

BAYESIAN INFERENCE

0
01 Dec 2020

Bidirectional Convolutional Poisson Gamma Dynamical Systems

NeurIPS 2020 BoChenGroup/BCPGDS

Incorporating the natural document-sentence-word structure into hierarchical Bayesian modeling, we propose convolutional Poisson gamma dynamical systems (PGDS) that introduce not only word-level probabilistic convolutions, but also sentence-level stochastic temporal transitions.

BAYESIAN INFERENCE VARIATIONAL INFERENCE

0
01 Dec 2020

Towards constraining warm dark matter with stellar streams through neural simulation-based inference

30 Nov 2020JoeriHermans/constraining-dark-matter-with-stellar-streams-and-ml

A statistical analysis of the observed perturbations in the density of stellar streams can in principle set stringent contraints on the mass function of dark matter subhaloes, which in turn can be used to constrain the mass of the dark matter particle.

BAYESIAN INFERENCE

4
30 Nov 2020

Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time

27 Nov 2020undark-lab/swyft

We present algorithms (a) for nested neural likelihood-to-evidence ratio estimation, and (b) for simulation reuse via an inhomogeneous Poisson point process cache of parameters and corresponding simulations.

BAYESIAN INFERENCE

36
27 Nov 2020

Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings

22 Nov 2020roads/psiz

The Human Similarity Judgments extension to ImageNet (ImageNet-HSJ) is composed of human similarity judgments that supplement the ILSVRC validation set.

BAYESIAN INFERENCE OBJECT RECOGNITION

11
22 Nov 2020

Sequential Likelihood-Free Inference with Implicit Surrogate Proposal

15 Oct 2020Kim-Dongjun/Sequential_Likelihood_Free_Inference_with_Implicit_Surrogate_Proposal

Bayesian inference without the access of likelihood, called likelihood-free inference, is highlighted in simulation to yield a more realistic simulation result.

BAYESIAN INFERENCE

0
15 Oct 2020

Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks

1 Oct 2020stefanradev93/AIAgainstCorona

Mathematical models in epidemiology strive to describe the dynamics and important characteristics of infectious diseases.

BAYESIAN INFERENCE EPIDEMIOLOGY

0
01 Oct 2020

Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent

ICLR 2021 kclip/DSVGD

This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-parametric generalized Bayesian inference framework for federated learning.

BAYESIAN INFERENCE FEDERATED LEARNING

1
11 Sep 2020