The following pages link to PyMC (Q22443):
Displaying 43 items.
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo (Q123703) (← links)
- Developing memory-based models of ACT-R within a statistical framework (Q826893) (← links)
- Predicting human behavior in unrepeated, simultaneous-move games (Q1682705) (← links)
- Bayesian model calibration and optimization of surfactant-polymer flooding (Q2009830) (← links)
- Visualizing the invisible: the effect of asymptomatic transmission on the outbreak dynamics of COVID-19 (Q2021038) (← links)
- On the permutation entropy Bayesian estimation (Q2025513) (← links)
- Julia language in machine learning: algorithms, applications, and open issues (Q2026295) (← links)
- Bayesian inference over the Stiefel manifold via the Givens representation (Q2057336) (← links)
- Recycling intermediate steps to improve Hamiltonian Monte Carlo (Q2057341) (← links)
- Learning context-dependent choice functions (Q2069057) (← links)
- A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew Gaussian processes (Q2071487) (← links)
- Rank-normalization, folding, and localization: an improved \(\widehat{R}\) for assessing convergence of MCMC (with Discussion) (Q2111117) (← links)
- Fundamental tools for developing likelihood functions within ACT-R (Q2116079) (← links)
- Rule-based Bayesian regression (Q2152549) (← links)
- Structured hierarchical models for probabilistic inference from perturbation screening data (Q2170453) (← links)
- The reproduction number of COVID-19 and its correlation with public health interventions (Q2221723) (← links)
- Is it safe to lift COVID-19 travel bans? The Newfoundland story (Q2221733) (← links)
- Approximate Bayesian computations to fit and compare insurance loss models (Q2234770) (← links)
- Random orthogonal matrices and the Cayley transform (Q2295044) (← links)
- Modified Hamiltonian Monte Carlo for Bayesian inference (Q2302498) (← links)
- Improving the efficiency and robustness of nested sampling using posterior repartitioning (Q2329804) (← links)
- Novel methods in computational finance (Q2361711) (← links)
- A paradigm for data-driven predictive modeling using field inversion and machine learning (Q2374961) (← links)
- Instantaneous turbulent kinetic energy modelling based on Lagrangian stochastic approach in CFD and application to wind energy (Q2672736) (← links)
- Numerical solution of the Fokker-Planck equation using physics-based mixture models (Q2674128) (← links)
- Bayesian physics informed neural networks for real-world nonlinear dynamical systems (Q2679296) (← links)
- Optimizing combination therapy in a murine model of HER2+ breast cancer (Q2679319) (← links)
- Direct sampling with a step function (Q2680305) (← links)
- BayesPy: variational Bayesian inference in Python (Q2810831) (← links)
- A general framework for constrained Bayesian optimization using information-based search (Q2834491) (← links)
- Searches for cosmic-string gravitational-wave bursts in Mock LISA Data (Q3162790) (← links)
- (Q4558473) (← links)
- Using Python to Analyse Financial Markets (Q4626526) (← links)
- Cosmological Model Independent Time Delay method (Q5023178) (← links)
- Approximate leave-future-out cross-validation for Bayesian time series models (Q5036890) (← links)
- Searching for axion-like particles through CMB birefringence from string-wall networks (Q5044826) (← links)
- Simulations of Social Distancing Scenarios and Analysis of Strategies to Predict the Spread of COVID-19 (Q5048318) (← links)
- Learning Hamiltonian Monte Carlo in R (Q5056999) (← links)
- Data-consistent inversion for stochastic input-to-output maps (Q5117405) (← links)
- Inference of a Mesoscopic Population Model from Population Spike Trains (Q5131155) (← links)
- Declarative Probabilistic Programming with Datalog (Q5276186) (← links)
- Extraction of Synaptic Input Properties <i>in Vivo</i> (Q5380817) (← links)
- An Invitation to Sequential Monte Carlo Samplers (Q5881159) (← links)
