This talk is about BLMM, a Python toolbox for parameter estimation and inference on big linear mixed models on NeuroImaging data. It was originally presented for the university of California spatial statistics reading group on November 10th, 2022.</p
Summary:Biological models contain many parameters whose values are difficult to measure directly via...
Statistical inference aims to quantify the amount of uncertainty in parameters or functions estimate...
<div><p>This work is in line with an on-going effort tending toward a computational (quantitative an...
This talk was given on BLMM, a framework for distributed computation of big linear mixed models on N...
As sample sizes grow, researchers face mounting pressure to detect and account for complex covarianc...
Big datasets such as IMAGEN (n=1,326), the Adolescent Brain Cognitive Development study (n=12,000) a...
SummaryBrain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) to...
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relyin...
This article provides an introduction into the statistical analysis of neuroimaging data using the g...
The Python programming language is steadily increasing in popularity as the language of choice for s...
The Python programming language is steadily increasing in popularity as the language of choice for s...
The glm-ie toolbox contains functionality for estimation and inference in generalised linear models ...
{The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and S...
<p>Poster presented at ECVP 2016 in Barcelona. Software will be made available at https://github.com...
With the arrival of the R packages \fontencoding {T1}\texttt {nlme} and \fontencoding {T1}\texttt {l...
Summary:Biological models contain many parameters whose values are difficult to measure directly via...
Statistical inference aims to quantify the amount of uncertainty in parameters or functions estimate...
<div><p>This work is in line with an on-going effort tending toward a computational (quantitative an...
This talk was given on BLMM, a framework for distributed computation of big linear mixed models on N...
As sample sizes grow, researchers face mounting pressure to detect and account for complex covarianc...
Big datasets such as IMAGEN (n=1,326), the Adolescent Brain Cognitive Development study (n=12,000) a...
SummaryBrain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) to...
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relyin...
This article provides an introduction into the statistical analysis of neuroimaging data using the g...
The Python programming language is steadily increasing in popularity as the language of choice for s...
The Python programming language is steadily increasing in popularity as the language of choice for s...
The glm-ie toolbox contains functionality for estimation and inference in generalised linear models ...
{The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and S...
<p>Poster presented at ECVP 2016 in Barcelona. Software will be made available at https://github.com...
With the arrival of the R packages \fontencoding {T1}\texttt {nlme} and \fontencoding {T1}\texttt {l...
Summary:Biological models contain many parameters whose values are difficult to measure directly via...
Statistical inference aims to quantify the amount of uncertainty in parameters or functions estimate...
<div><p>This work is in line with an on-going effort tending toward a computational (quantitative an...
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