Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks

Type de record:

paperposter

SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks

Poster presented at the Deep Learning Indaba 2022 by Jacobie Mouton

Visit

storage.googleapis.com

Tags

deep learning indabaposterdlideep learning indaba 2022

Similaires

Isadare-Oreoluwa/Leveraging-Machine-Learning-for-Predicting-Agricultural-Trade-FlowsIdentification and Bayesian Inference for Synthetic Control Methods with Spillover EffectsBayesian inference of Latent Spectral ShapesBayesian Inference for Logistic Regression Models Using Sequential Posterior SimulationBayesian Inference for a Semi-Parametric Copula-based Markov ChainInference of historical influence networks

Isadare-Oreoluwa/Leveraging-Machine-Learning-for-Predicting-Agricultural-Trade-Flows

This project applies machine learning techniques to predict agricultural trade flows between Nigeria

Identification and Bayesian Inference for Synthetic Control Methods with Spillover Effects

The synthetic control method (SCM) is widely used for causal inference with panel data, particularly

Bayesian inference of Latent Spectral Shapes

This paper proposes a hierarchical spatial-temporal model for modelling the spectrograms of animal c

Bayesian Inference for Logistic Regression Models Using Sequential Posterior Simulation

The logistic specification has been used extensively in non-Bayesian statistics to model the depende

Bayesian Inference for a Semi-Parametric Copula-based Markov Chain

This paper presents a method to specify a strictly stationary univariate time series model with part

Inference of historical influence networks

We study the romanization process of northern Africa from 50 BC till 300 AD. Our goal is to infer th