About Janet van Niekerk Janet van Niekerk Research Scientist (former), Statistics Bayesian computational statistics Survival analysis Biostatistics Events Presented Events Sep 28 - Oct 4, 2025 Event Status: Cancelled | Efficient Bayesian Methods for Biostatistics Janet van Niekerk, Research Scientist (former), Statistics Oct 2, 12:00 - 13:00 B9 L2 R2325 bayesian methods In this talk, I will present some case studies where we approach near real-time inference for complex Biostatistics models, such as disease mapping and brain activation mapping models, among others often encountered in the biostatistics domain, using INLA. May 5 - May 11, 2019 Joint longitudinal-survival models using R-INLA Janet van Niekerk, Research Scientist (former), Statistics May 9, 12:00 - 13:00 B9 L2 H1 stochastic smoothing pipeline R-INLA Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. They gained popularity amongst practitioners by the ability to incorporate various data sources. In this talk, we will introduce joint models and provide some conceptual ideas about their use and necessity. Also, we will illustrate how these models can be formulated as Latent Gaussian Models and hence be implemented using R-INLA.
Event Status: Cancelled | Efficient Bayesian Methods for Biostatistics Janet van Niekerk, Research Scientist (former), Statistics Oct 2, 12:00 - 13:00 B9 L2 R2325 bayesian methods In this talk, I will present some case studies where we approach near real-time inference for complex Biostatistics models, such as disease mapping and brain activation mapping models, among others often encountered in the biostatistics domain, using INLA.
Joint longitudinal-survival models using R-INLA Janet van Niekerk, Research Scientist (former), Statistics May 9, 12:00 - 13:00 B9 L2 H1 stochastic smoothing pipeline R-INLA Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. They gained popularity amongst practitioners by the ability to incorporate various data sources. In this talk, we will introduce joint models and provide some conceptual ideas about their use and necessity. Also, we will illustrate how these models can be formulated as Latent Gaussian Models and hence be implemented using R-INLA.
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