Fitting latent variable models

Fitting latent variable models involves finding hidden factors that influence observed data. These models assume some underlying, unobserved variables explain patterns in what we see, like underlying skills affecting test scores or emotions influencing behaviors. The process adjusts the model’s parameters so that the predicted data aligns closely with actual data. This is typically done using statistical algorithms that iteratively improve the fit, helping us understand complex relationships and uncover hidden structures within data. Essentially, it's about uncovering unseen factors that shape observed outcomes, improving our ability to analyze and interpret real-world phenomena.