BKP: An R package for beta kernel process modeling
Probability modeling, uncertainty quantification, and an R implementation.
STATISTICS · METHODS · COMPUTATION
Jiangyan Zhao赵江彦
Postdoctoral Researcher in Statistics
East China Normal University · Shanghai
Simple models. Clear uncertainty.
Practical decisions.
Research philosophy / KISS
KEEP IT
SIMPLE
AND SMART
A principle for doing research
Complex problems do not always require complex models.
I focus on statistical methods that are simple, transparent, computationally practical, and useful for real decisions.
Research areas
Questions that connect.
01Statistical Learning
Bayesian Statistics
How can we learn from data while accounting for uncertainty?
02Statistical Learning
& Optimization
How can statistical learning support better decisions?
03Biostatistics
How can statistical methods inform health and biomedical research?
Connected through statistical computing and practical software.
Selected work
Methods, in practice.
Bayesian optimization via exact penalty
Statistical learning for optimization under constraints.
A Bayesian phase I/II platform design with survival efficacy endpoint for dose optimization
Bayesian methodology for clinical trial design and dose optimization.
Software / BKP
From a method
to a tool.
BKP is an R package for beta kernel process modeling. Explore the software, its documentation, and the examples behind the method.