Presented by Associate Professor Chris Drovandi
Complex statistical models are abundant in many areas such as ecology, biology and finance, and often possess several unknown parameters. A key task in statistical inference is to estimate the parameters based on observed data, which is often based on the likelihood function. Once a statistical model is ‘calibrated’, it can be used for hypothesis testing and prediction, and can inform decision making. However, off-the-shelf parameter estimation methods are often not applicable for complex models due to computational intractability of their likelihood functions. Fortunately, despite likelihood intractability, it is often feasible to simulate from complex models for any given parameter value. This talk will take a look under the hood at some parameter estimation methods, such as approximate Bayesian computation and Bayesian synthetic likelihood, that are applicable when only model simulation is feasible, and discuss some challenges.
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| Start Date: | 26/06/2020 [add to calendar] |
| Start Time: | 2pm |
| End Date: | 26/06/2020 |
| End Time: | 3pm |