Model Criticism in Latent Space

11/13/2017
by   Sohan Seth, et al.
0

Model criticism is usually carried out by assessing if replicated data generated under the fitted model looks similar to the observed data, see e.g. Gelman, Carlin, Stern, and Rubin (2004, p. 165). This paper presents a method for latent variable models by pulling back the data into the space of latent variables, and carrying out model criticism in that space. Making use of a model's structure enables a more direct assessment of the assumptions made in the prior and likelihood. We demonstrate the method with examples of model criticism in latent space applied to ANOVA, factor analysis, linear dynamical systems and Gaussian processes.

READ FULL TEXT
research
06/30/2016

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

Unsupervised learning on imbalanced data is challenging because, when gi...
research
04/19/2021

Simulation-Based Inference with Approximately Correct Parameters via Maximum Entropy

Inferring the input parameters of simulators from observations is a cruc...
research
09/26/2013

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in...
research
06/28/2018

Single Index Latent Variable Models for Network Topology Inference

A semi-parametric, non-linear regression model in the presence of latent...
research
05/23/2018

Learning latent variable structured prediction models with Gaussian perturbations

The standard margin-based structured prediction commonly uses a maximum ...
research
01/22/2019

Modelling Numerical Systems with Two Distinct Labelled Output Classes

We present a new method of modelling numerical systems where there are t...
research
08/22/2016

LFADS - Latent Factor Analysis via Dynamical Systems

Neuroscience is experiencing a data revolution in which many hundreds or...

Please sign up or login with your details

Forgot password? Click here to reset