A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values
We consider the problem of learning parameters of latent variable models from mixed (continuous and ordinal) data with missing values. We propose a novel Bayesian Gaussian copula factor (BGCF) approach that is consistent under certain conditions and that is quite robust to the violations of these conditions. In simulations, BGCF substantially outperforms two state-of-the-art alternative approaches. An illustration on the `Holzinger & Swineford 1939' dataset indicates that BGCF is favorable over the so-called robust maximum likelihood (MLR) even if the data match the assumptions of MLR.
Code (2)
Tasks
Missing ValuesSimilar Papers 제목 키워드 기반
General Latent Feature Modeling for Data Exploration Tasks
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either disc…
Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables
Although Bayesian Optimization (BO) has been employed for accelerating materials design in computational materials engineering, existing works are restricted to problems with quantitative variables. However, real designs…
Bayesian OptimizationA comparison of mixed-variables Bayesian optimization approaches
Most real optimization problems are defined over a mixed search space where the variables are both discrete and continuous. In engineering applications, the objective function is typically calculated with a numerically c…
Bayesian OptimizationGaussian ProcessesHybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization
This paper presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed n…
Bayesian OptimizationGaussian ProcessesModel SelectionPositionMold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
Real-world optimization problems are generally not just black-box problems, but also involve mixed types of inputs in which discrete and continuous variables coexist. Such mixed-space optimization possesses the primary c…
Bayesian OptimizationComputational EfficiencyGraph structure learning