ASCEND

BY NTHRYS

NTHRYS › Internships › Probability Theory

Markov Chain Monte Carlo Methods

Probability Theory

Markov Chain Monte Carlo Methods

Focused area

Variant

Pay · Join

Step 3 of 5Choose focused area

Field

▾

Category

▾

Focused area

▾

You might prefer

AI Stochastic Process Research Machine Learning Bayesian Inference Research AI Large Deviation Theory Research AI Martingale Theory Research AI Random Matrix Theory Research AI Concentration Inequality Research AI Point Process Theory Research AI Statistical Learning Theory Research AI Diffusion Process Research AI Probabilistic Graphical Model Research Quantum Probability Theory Applications Copula Theory and Dependence Modeling Branching Process Dynamics Research Extreme Value Theory and Tail Risk Monte Carlo Simulation Method Development Stochastic Differential Equation Modeling Renewal Theory and Reliability Analysis Brownian Motion and Geometric Brownian Motion Hidden Markov Model Inference Research Poisson Process Applications and Extensions Random Walk Theory and Applications Probabilistic Inference in Graphical Models Variational Inference and Approximation Methods Gaussian Process Theory and Implementation Importance Sampling and Resampling Techniques Bootstrap Methods and Resampling Analysis Expectation Maximization Algorithm Research Time Series Forecasting with Probabilistic Models Survival Analysis and Hazard Modeling Empirical Process Theory Research Information Theory and Divergence Measures Coupling Methods and Probability Bounds Lévy Process Theory and Applications Gaussian Random Field Simulation Methods Inverse Problems and Uncertainty Quantification Network Reliability and Graph Probability Limit Theorems and Convergence Analysis Spatial Point Process Modeling Research Probabilistic Cellular Automata Research Adaptive Sampling and Optimal Design Spectral Methods for Markov Processes Probabilistic Forecasting and Calibration Rare Event Simulation and Estimation Stochastic Optimization and Control Gibbs Sampling and Spin Systems Mixing Times and Spectral Gap Analysis Probabilistic Graphical Model Inference Optimization Anomaly Detection via Probabilistic Methods Stochastic Gradient Descent Theory

Markov Chain Monte Carlo Methods

Develop and analyze MCMC algorithms including Gibbs sampling and Metropolis-Hastings for Bayesian inference applications.

The focused areas below are internship topics in varied working formats. Pick one, then choose your internship type, mode… Read more

Loading focused areas...