Loss convergence in a causal Bayesian neural network of retail firm performance

08/29/2020
by   F. Trevor Rogers, et al.
0

We extend the empirical results from the structural equation model (SEM) published in the paper Assortment Planning for Retail Buying, Retail Store Operations, and Firm Performance [1] by implementing the directed acyclic graph as a causal Bayesian neural network. Neural network convergence is shown to improve with the removal of the node with the weakest SEM path when variational inference is provided by perturbing weights with Flipout layers, while results from perturbing weights at the output with the Vadam optimizer are inconclusive.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/14/2021

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

Learning the causal structure that underlies data is a crucial step towa...
research
12/06/2021

BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery

A structural equation model (SEM) is an effective framework to reason ov...
research
08/09/2019

Convergence Rates of Variational Inference in Sparse Deep Learning

Variational inference is becoming more and more popular for approximatin...
research
11/15/2022

On the Performance of Direct Loss Minimization for Bayesian Neural Networks

Direct Loss Minimization (DLM) has been proposed as a pseudo-Bayesian me...
research
11/04/2022

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes

Bayesian causal structure learning aims to learn a posterior distributio...
research
11/02/2021

DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks

Causal structures for observational survival data provide crucial inform...
research
12/10/2018

Bayesian Layers: A Module for Neural Network Uncertainty

We describe Bayesian Layers, a module designed for fast experimentation ...

Please sign up or login with your details

Forgot password? Click here to reset