9 People Results for the Tag: Markov Chains

All A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

David Hughes

Associate Professor of Entomology and Biology
Parasite manipulation of host behavior

Center for Infectious Disease Dynamics

Dezhe Jin

Associate Professor of Physics
Computational models of neural basis of motor control and learning; theoretical analysis of biological neural networks.

Tanya Renner

Assistant Professor of Entomology
Evolution of chemical and structural defense. Molecular evolution, evolutionary genomics, and transcriptomics. Origins and evolution of carnivorous plants.

David R Hunter

Professor of Statistics, Department Head
I work on statistical models for networks, mixture models, and certain optimization algorithms called MM algorithms. A full list of papers and related work, such as software, may be found at http://sites.stat.psu.edu/~dhunter/.

Center for Infectious Disease Dynamics

Ephraim M. Hanks

Associate Professor of Statistics
Spatio-Temporal Statistics. Bayesian Hierarchical Modeling. Optimal Sampling. Animal Movement. Landscape Genetics.

Center for Infectious Disease Dynamics

Murali Haran

Associate Professor of Statistics
Statistical computing (Markov chain Monte Carlo algorithms); spatial models (Gaussian random field models); methods for complex computer models; interdisciplinary collaborations in environmental sciences, climate science, disease modeling, ecology

Center for Infectious Disease Dynamics

Nita Bharti

Huck Early Career Professor; Assistant Professor of Biology
The underlying mechanisms for spatial heterogeneities in host disease burden and risk across spatial scales, from regional dynamics to seasonal outbreaks in cities, rural villages, and across borders by assessing regional variations in movement and contact patterns relating to outbreaks and access to health care.

Center for Infectious Disease Dynamics

Tom Baker

Distinguished Professor of Entomology and Chemical Ecology

Lynn Lin

Assistant Professor of Statistics
Bayesian mixture model development for classification, variable selection, design and model selection, structured and hierarchical non-parametric Bayesian methods, rare event detection, and statistical computation involving simulation and optimization.