Podcasts by Collection

AdaptEllipticalSliceSampler.jl Permalink

A Julia package implementing the adaptive generalized elliptical slice sampler (AGESS), a tuning-free, gradient-free MCMC algorithm for gradient-free Bayesian inference.

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Germline variants disrupting microRNAs predict long-term genitourinary toxicity after prostate cancer radiation (2022)

A.U. Kishan, N. Marco, M.B. Schulz-Jaavall, M.L. Steinberg, P.T. Tran, J.E. Juarez, et al., J.B. Weidhaas

Germline biomarkers predict toxicity to anti-PD1/PDL1 checkpoint therapy (2022)

J. Weidhaas, N. Marco, A.W. Scheffler, A. Kalbasi, K. Wilenius, E. Rietdorf, et al., D. Telesca

Viral burden and clearance in asymptomatic COVID-19 Patients (2022)

A.B. Gunatilaka, N. Marco, G.H. Read, M. Sweeney, G. Regan, C. Tsang, et al., J.B. Weidhaas

Application of a genetic signature of late GU toxicity in SCIMITAR, a Post-op SBRT trial (2023)

A.U. Kishan, N. Marco, T.M. Ma, M.L. Steinberg, A. Sachdeva, M. Cao, L.K. Ballas, E. Rietdorf, D. Telesca, J.B. Weidhaas

Early findings from the GARUDA trial: The impact of a genetic signature of late radiation toxicity on prostate cancer treatment decision making. (2023)

J.B. Weidhaas, N. Marco, M.L. Steinberg, A. Lee, M. Xiang, L.F. Valle, M. Casado, A. Stube, D. Telesca, A.U. Kishan

Functional Mixed Membership Models (2024)

N. Marco, D. Şentürk, S. Jeste, C. DiStefano, A. Dickinson, D. Telesca

Flexible Regularized Estimation in High-Dimensional Mixed Membership Models (2024)

N. Marco, D. Şentürk, S. Jeste, C.C. DiStefano, A. Dickinson, D. Telesca

Modeling Neural Switching via Drift-Diffusion Models (2024)

N. Marco, J.M. Groh, S.T. Tokdar

Bayesian Covariance Regression in Functional Data Analysis with Applications to Functional Brain Imaging (2025)

J. Shamshoian, N. Marco, D. Şentürk, S. Jeste, D. Telesca

Germline microRNA-based signatures predict toxicity and response to anti-CTLA-4 therapy (2025)

J.B. Weidhaas, K.M. McGreevy, N. Marco, N. Sundahl, C.R. Cabanski, C. Spencer, T. LaVallee, P. Ost, D. Telesca

Covariate Adjusted Functional Mixed Membership Models (2025)

N. Marco, D. Şentürk, S. Jeste, C. DiStefano, A. Dickinson, D. Telesca

Mixed Membership Models for Multilevel Functional Data (2026)

D. Telesca, N. Marco, E. Landry

Adaptive Generalized Elliptical Slice Sampling (2026)

N. Marco, S.T. Tokdar

Neural multiplexing vs place coding: Multiplexing steps in when most needed (2026)

J.M. Leeman, S.M. Willett, N. Marco, S.T. Tokdar, J.M. Groh

Bayesian Functional Partial Membership Models

Published: June 24, 2021

We propose a model which allows for each observation to partially belong to multiple clusters. In this model, we assume that each observation is functional. We assume an additive model with respect to the cluster membership. In order to reduce the number of parameters, we use a covariance function that is inspired by Multivariate Functional Principal Component Analysis.