Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
Bayesian Inference in Epidemic Modelling: A Beginner’s Guide Illustrated with the SIR Model
PhD Mathematics
30秒速读
IN SHORT: This guide addresses the core challenge of estimating uncertain epidemiological parameters (like transmission and recovery rates) from noisy, real-world outbreak data by providing a clear, applied pathway using Bayesian inference and MCMC.
核心创新
- Methodology Presents an integrated, pedagogical pipeline from the SIR ODE model through Bayesian likelihood formulation to practical MCMC implementation, demystifying the process for beginners.
- Methodology Explicitly connects the Gaussian noise assumption in the likelihood to the common least-squares fitting approach, framing Bayesian inference as its natural probabilistic extension with uncertainty quantification.
- Theory Emphasizes the interpretative power of the full posterior distribution and credible intervals over single point estimates, highlighting this as the key advantage for decision-making under uncertainty.
主要结论
- In a synthetic example with true parameters β=0.3, γ=0.1 (R0=3.0), MCMC recovered posterior means of β=0.300 (std 0.002) and γ=0.102 (std 0.001), demonstrating accurate and precise inference.
- The posterior distribution for R0 was estimated as 2.95 with a standard deviation of 0.03, showing the method successfully quantifies uncertainty in this critical epidemiological metric.
- The framework successfully separates the roles of individual parameters β and γ, showing that different pairs can yield the same R0 but produce distinct epidemic curve shapes (e.g., peak sharpness), which point estimates alone would miss.
摘要: This guide provides a beginner-friendly introduction to Bayesian inference in the context of epidemic modeling, using the classic Susceptible-Infected-Recovered (SIR) model as a working example. It covers the mathematical setup of the SIR ordinary differential equations, the formulation of the Bayesian inference problem (likelihood and prior specification), and the implementation of Markov Chain Monte Carlo (MCMC) via the Metropolis-Hastings algorithm to estimate transmission (β) and recovery (γ) rates from noisy outbreak data. The tutorial emphasizes the conceptual advantages of the Bayesian framework—which provides full posterior distributions quantifying parameter uncertainty—over frequentist point estimates, and walks through a complete synthetic example with results and interpretation.