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Found 4 essays matching the above criteria.
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1. Autoencoder-Based Likelihood-Free Parameter Inference of Gene Regulatory Network
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Likelihood-free parameter inference is a well-known statistical methodology that estimates the posterior distribution of model parameters even in cases where the likelihood function is intractable. The performance of this method is highly correlated with the learning of summary statistics, which capture the key features from the high dimensional data such as time series. READ MORE
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2. Calibration of Breast Cancer Natural History Models Using Approximate Bayesian Computation
University essay from KTH/Matematisk statistikAbstract : Natural history models for breast cancer describe the unobservable disease progression. These models can either be fitted using likelihood-based estimation to data on individual tumour characteristics, or calibrated to fit statistics at a population level. READ MORE
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3. Bayesian Parameterization in the spread of Diseases
University essay from Uppsala universitet/Avdelningen för beräkningsvetenskapAbstract : Mathematical and computational epidemiological models are important tools in efforts to combat the spread of infectious diseases. The models can be used to predict further progression of an epidemic and for assessing potential countermeasures to control disease spread. READ MORE
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4. Likelihood-free inference and approximate Bayesian computation for stochastic modelling
University essay from Lunds universitet/Matematisk statistikAbstract : With increasing model complexity, sampling from the posterior distribution in a Bayesian context becomes challenging. The reason might be that the likelihood function is analytically unavailable or computationally costly to evaluate. READ MORE
