Latent Variable Models are statistical methods used to identify unseen factors affecting observable data, especially when direct measurement is difficult. They are applied in psychology, sociology, economics, and AI. Techniques like Factor Analysis, Growth Curve Modelling, and Generalised Latent Variable Modelling reveal underlying structures, temporal dynamics, and complex relationships in data. These models are crucial for research in genomics, marketing, language processing, and more.
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Latent Variable Models are statistical techniques used to uncover hidden factors in data
Psychology and marketing
Latent Variable Mixture Modelling is used in psychology and marketing to identify subgroups within populations
Genomics
Bayesian Latent Variable Models are used in genomics to understand complex genetic structures
Language processing and financial forecasting
Recurrent Latent Variable Models are used in language processing and financial forecasting to capture time-dependent patterns and hidden states
Generalised Latent Variable Modelling expands the scope of traditional models to accommodate diverse data types and relationships
Factor analysis simplifies complex data by revealing latent factors that account for patterns of correlation among observed variables
The process involves extracting a correlation matrix and identifying a smaller number of unobserved variables that can explain these correlations
Factor loadings indicate the strength of the association between observed variables and identified latent factors, providing a clearer understanding of the data's underlying dimensions
Growth Curve Modelling integrates time as a core element, allowing for the analysis of longitudinal data and capturing individual differences in development or progress
This method distinguishes between fixed effects, which are consistent across a population, and random effects, which vary among individuals
By using polynomial functions, growth curve models can effectively capture individual differences in development or progress over time
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Latent Variable Models application areas
Used in psychology, sociology, economics, AI to analyze unmeasurable variables.
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Example of latent variables in psychology
Intelligence, anxiety estimated through test responses, not directly observable.
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Inference method in Latent Variable Models
Infers unseen factors' presence and effects by examining observed variable relationships.
