Statistics seminars are informal events where new ideas can be presented and discussed. The aim is to provide a venue for discussion and exchange of ideas between different statistics research groups and others interested in statistics. All seminars are in English. The program is updated continuously so look out for new titles.
Time: Fortnightly on Tuesdays at 11:15 - 12:00.
Place: Exactum C124.
All interested are warmly welcome!
- 25.9. canceled
- 9.10. Joseph Sakaya
Lambert Matrix Factorization
- 23.10. Hande Topa
Gaussian process modelling of genome-wide high-throughput sequencing time series
- 6.11. Tomasz Kusmierczyk
Loss-calibrated variational inference
- 20.11. Gleb Tikhonov
Computationally efficient joint species distribution modeling of big spatial data – advances and challenges
- 4.12. Malcolm Itter
- 18.12. Elina Numminen
- 23.1. Mikko Heikkilä
Differentially private Bayesian learning on distributed data
- 6.2. Umberto Simola
An Adaptive Approximate Bayesian Computation Tolerance Selection Algorithm
- 20.2. School holidays break
- 6.3. Jussi Mäkinen
Hierarchical Bayesian framework for inferring species densities from heterogeneous observations
- 20.3. Liisa Iivonen
Novel Bayesian models for past climate reconstruction from pollen records
- 17.4. Jia Liu
Bayesian model based spatio-temporal sampling design with an application on species distribution modeling
- 15.5. Timothy E. O’Brien at 10-12 (Notice the unusual time)
Statistical Modelling and Design: From Theory to Practice
Researchers often find that nonlinear regression models are more applicable for modelling various biological, physical and chemical processes than are linear ones since they tend to fit the data well and since these models (and model parameters) are more scientifically meaningful. These researchers are thus often in a position of requiring optimal or near-optimal designs for a given nonlinear model. A common shortcoming of most optimal designs for nonlinear models used in practical settings, however, is that these designs typically focus only on (first-order) parameter variance or predicted variance, and thus ignore the inherent nonlinear of the assumed model function. Another shortcoming of optimal designs is that they often have only support points, where is the number of model parameters.
Furthermore, measures of marginal curvature, first introduced in Clarke (1987) and extended in Haines et al (2004), provide a useful means of assessing this nonlinearity. Other relevant developments are the second-order volume design criterion introduced in Hamilton and Watts (1985) and extended in O’Brien (2010), and the second-order MSE criterion developed and illustrated in Clarke and Haines (1995).
In the context of applied statistical modelling, this talk examines various robust design criteria and those based on second-order (curvature) considerations. These techniques, coded in popular software packages, are illustrated with several examples including one from a preclinical dose-response setting encountered in a recent
- 19.9. Jarno Vanhatalo
Spatial Clustering Using Gaussian Processes Embedded in a Mixture Model
- 3.10. Joonas Jälkö
Privacy aware variational inference
- 17.10. Marcelo Hartmann
Approximate inference for location-scale Gaussian process regression with Student-t probabilistic model
- 24.10. Juha Karvanen (Notice! this is extra between two normal schedule seminars)
Towards automated causal inference
- 31.10. Johan Pensar
Structure learning of context-specific graphical models
- 14.11. no seminar
- 28.11. Tommi Mäklin
Probabilistic quantification of bacterial strain mixtures
- 12.12. Christian Benner
Efficient variable selection among thousands of correlated genetic variants using summary data from genome-wide association studies