Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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.