Estimating R

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Explores neural network simulation, activity dynamics, and validation processes to ensure accurate predictions.

Optimization and Simulation

Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.

Equilibrium and Distribution

Explores the mesoscopic master equation, equilibrium, distribution, and Metropolis algorithm in Monte Carlo simulations.

Comparison of Noise Models

Compares diffusive noise and escape noise models in computational neuroscience, discussing simulation, calculation, and model fitting.

Spectral analysis and stationarity tests

Covers spectral analysis, stationarity tests, challenges of nonstationary time series, and tools for analysis with missing data.

Molecular Dynamics: Liquid Argon Simulation

Covers the molecular dynamics simulation of liquid argon using Lennard-Jones potential and focuses on equilibration and velocity distribution at equilibrium.

Estimating R: Marking and Convergence

Covers the estimation of R in Poisson processes, focusing on marking points and convergence.

Data reconciliation and parameter identification by FM

Explores measurement analysis, data reconciliation, and parameter identification in energy systems, emphasizing the importance of correct measurements and optimization.

Coarse-Grained Simulations: Basics and Applications

Covers coarse-grained simulations, including advantages, schemes, DPD algorithm, lipid membrane coarse-graining, and setting interaction parameters.

QUCS Parameter Sweeps

Covers the process of doing parameter sweeps in QUCS, focusing on setting up and configuring the simulation type 'parameter sweep' with linear values.