Komorebi AI

Variationally Inferred Sampling through a Refined Bound

MDPI – Entropy, 2022

Abstract

This study introduces a «refined variational approximation» framework, which enhances the efficiency of Bayesian inference in probabilistic models by embedding a Markov chain sampler within a variational posterior approximation. 

Notably easy to implement, it elevates the mixing speed by tuning sampler parameters using automatic differentiation.

A new framework that enhances Bayesian inference in probabilistic models enhancing the efficiency of decision-making processes in various fields including financial analysis, risk management, machine learning, and logistics.

Where does it apply?

It can be applied in financial and risk analysis, where state-space time series models are particularly useful, data science for machine learning applications like variational autoencoders, and various other sectors that rely on data-informed decision making, such as logistics, energy, healthcare, and technology sectors among others.

Variationally Inferred Sampling through a Refined Bound

Why does it matters?

It allows for efficient large-scale Bayesian inference in probabilistic models, providing a general approach to tuning Markov chain Monte Carlo sampler parameters.

This innovation improves the applicability of models, such as state-space time series and variational autoencoders. Furthermore, low-cost, convenient evidence lower bound (ELBO) approximations are an integral part of this achievement. 

These advancements could potentially optimize Bayesian modeling, thus significantly impacting inference, prediction, and decision-making processes in various fields.

Variationally Inferred Sampling through a Refined Bound

MDPI – Entropy, 2022

Variationally Inferred Sampling through a Refined Bound

Otras publicaciones

pesca inteligente
Assessing the effect of advertising expenditures upon sales: A Bayesian structural time series model
procesamiento del lenguaje natural

TUN-AI

Evaluación del efecto de la inversión en publicidad sobre las ventas

Generación personalizada de imagen a partir de texto a usando gradientes estéticos

Un método de Aprendizaje Automático, para la estimación precisa de la biomasa de atún; beneficiando a la industria pesquera, organizaciones de gestión pesquera y la pesca sostenible.

Predicciones precisas de ventas basadas en la inversión en publicidad para ayudar en la toma de decisiones  para la planificación estratégica de la inversión publicitaria, asignación de presupuestos y planificación de campañas de marketing.

El estudio introduce la metodología de Gradientes Estéticos para personalizar modelos generativos de difusión, mejorando la experiencia del usuario e impactando campos como el arte digital, el diseño y el marketing.