Flexible methods for latent variable models applied to Health Economics

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This project aims to develop flexible and powerful methods for estimating models containing variables that are unobserved, that is, latent. Such models are often used to capture individual heterogeneity and time dependence in data collected on individuals, with each individual observed for several time periods. Latent variables can also infer group membership, where such membership is unavailable from the data. The intended methodology is Bayesian and based on new particle methods that allow users to select between models and predict future observations even in complex situations. The research aims to inform decision making through improved use of data in health economics and related fields.