Microsimulation models (MSMs) are used to inform policy by predicting population-level
outcomes under different scenarios. MSMs simulate individual-level event histories that …

Microsimulation models that describe disease processes synthesize information from
multiple sources and can be used to estimate the effects of screening and treatment on …

Mathematical health policy models, including microsimulation models (MSMs), are widely
used to simulate complex processes and predict outcomes consistent with available data …

Background Microsimulation models are mathematical models that simulate event histories
for individual members of a population. They are useful for policy decisions because they …

Calibration of a microsimulation model (MSM) is a challenging but crucial step for the
development of a valid model. Numerous calibration methods for MSMs have been …

Microsimulation is fast becoming the approach of choice for modelling and analysing
complex processes in the absence of mathematical tractability. While this approach has …

Background. Microsimulation models (MSMs) for health outcomes simulate individual event
histories associated with key components of a disease process; these simulated life histories …

Approximate Bayesian computation enables inference for complicated probabilistic models
with intractable likelihoods using model simulations. The Markov chain Monte Carlo …

Approximate Bayesian computation (ABC) is a class of simulation-based statistical inference
procedures that are increasingly being applied in scenarios where the likelihood function is …

A major stumbling block in multivariate discrete data analysis is the problem of evaluating
the outcome probabilities that enter the likelihood function. Calculation of these probabilities …