Information-theoretic latent distribution modeling: Distinguishing discrete and continuous latent variable models
Information-theoretic latent distribution modeling: Distinguishing discrete and continuous latent variable models
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DOI：
10.1037/1082-989x.11.3.228
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发表时间：
2006-09-01
影响因子：
7
通讯作者：
Krueger, Robert F.
中科院分区：
文献类型：
--
作者：
Markon, Kristian E.;Krueger, Robert F.
离散和连续潜变量分布之间的区别在行为科学的许多领域变得越来越重要。在这里，作者探讨了一种信息理论的方法，潜在的分布建模，其中强调的能力，潜在的分布模型表示统计信息的观测数据。作者的结论是，统计信息的损失与潜值的数量减少提供了一个有吸引力的基础，比较离散和连续的潜变量模型。理论上的考虑，以及2 Monte Carlo模拟的结果表明，信息理论提供了一个良好的基础，建模潜在的分布和区分离散和连续的潜变量模型，特别是。
Distinguishing between discrete and continuous latent variable distributions has become increasingly important in numerous domains of behavioral science. Here, the authors explore an information-theoretic approach to latent distribution modeling, in which the ability of latent distribution models to represent statistical information in observed data is emphasized. The authors conclude that loss of statistical information with a decrease in the number of latent values provides an attractive basis for comparing discrete and continuous latent variable models. Theoretical considerations as well as the results of 2 Monte Carlo simulations indicate that information theory provides a sound basis for modeling latent distributions and distinguishing between discrete and continuous latent variable models in particular.