What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical analysis
- Quantum mechanics
George Yin mainly investigates Markov chain, Mathematical optimization, Applied mathematics, Control theory and Markov process.
His Markov chain research incorporates themes from Geometric Brownian motion, Markov decision process and Portfolio.
The Mathematical optimization study combines topics in areas such as Stochastic approximation and State space.
His research in Applied mathematics intersects with topics in Mathematical analysis, Exponential function, Ergodicity and Diffusion.
His work in Control theory addresses issues such as System identification, which are connected to fields such as Nonlinear system, Identifiability, Estimation theory and State of charge.
His Markov process research integrates issues from Singular perturbation, Dynamical systems theory, Linear-quadratic-Gaussian control and Asymptotic expansion.
His most cited work include:
- Stochastic Approximation and Recursive Algorithms and Applications (1539 citations)
- Stochastic Approximation Algorithms and Applications (1038 citations)
- Markowitz's Mean-Variance Portfolio Selection with Regime Switching: A Continuous-Time Model (392 citations)
What are the main themes of his work throughout his whole career to date?
George Yin mainly focuses on Markov chain, Mathematical optimization, Applied mathematics, Control theory and Algorithm.
His studies link Markov process with Markov chain.
His Mathematical optimization study combines topics in areas such as Stochastic process, Stochastic approximation and Weak convergence.
His Applied mathematics research incorporates elements of Convergence, Nonlinear system, Work and Mathematical analysis.
His Mathematical analysis research includes elements of Diffusion process, Jump and Ergodicity.
He combines subjects such as Estimation theory, Stability and System identification with his study of Control theory.
He most often published in these fields:
- Markov chain (32.87%)
- Mathematical optimization (30.81%)
- Applied mathematics (28.92%)
What were the highlights of his more recent work (between 2015-2021)?
- Applied mathematics (28.92%)
- Markov chain (32.87%)
- Work (9.81%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Applied mathematics, Markov chain, Work, Convergence and Statistical physics.
George Yin specializes in Applied mathematics, namely Stochastic differential equation.
His biological study spans a wide range of topics, including Markov process, Mathematical analysis, Mathematical optimization, Optimal control and State space.
His Mathematical optimization study integrates concerns from other disciplines, such as Numerical analysis and Viscosity solution.
A large part of his Convergence studies is devoted to Rate of convergence.
The study incorporates disciplines such as Algorithm and Stochastic approximation in addition to Rate of convergence.
Between 2015 and 2021, his most popular works were:
- Coexistence and exclusion of stochastic competitive Lotka–Volterra models (49 citations)
- Classification of Asymptotic Behavior in a Stochastic SIR Model (47 citations)
- Conditions for Permanence and Ergodicity of Certain Stochastic Predator-Prey Models (46 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Quantum mechanics
His primary areas of study are Applied mathematics, Ergodicity, Markov chain, Convergence and Mathematical analysis.
George Yin interconnects Stochastic partial differential equation, Nonlinear system, Moment and Exponential function in the investigation of issues within Applied mathematics.
In most of his Markov chain studies, his work intersects topics such as Weak convergence.
His studies in Mathematical analysis integrate themes in fields like Diffusion process, Markov process and Markov property.
His Rate of convergence research includes themes of Mathematical optimization, Subgradient method and Diffusion.
Much of his study explores Mathematical optimization relationship to Stochastic approximation.
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