Nature

A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation

Experimental data and mathematical models are beginning to take equal billing in systems biology. Experimental observations without a framework in which to link them offer researchers only limited ...
JSTOR Daily

Profile-Kernel Likelihood Inference with Diverging Number of Parameters

The Annals of Statistics, Vol. 36, No. 5 (Oct., 2008), pp. 2232-2260 (29 pages) The generalized varying coefficient partially linear model with a growing number of predictors arises in many ...
JSTOR Daily

PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals

We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods ...
ascopubs.org

Postprediction Inference for Clinical Characteristics Extracted With Machine Learning on Electronic Health Records

Real-world data (RWD) derived from electronic health records (EHRs) are often used to understand population-level relationships between patient characteristics and cancer outcomes. Machine learning ...
University of Bristol

Point estimates and population parameters

Explain what is meant by statistical inference. Define a point estimate and population parameter and list common types of point estimates and parameters Identify point estimates and parameters when ...