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- Thomas G. Kurtz

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
50
Citations
20,665
126
World Ranking
768
National Ranking
385

2020 - Fellow of the American Mathematical Society For research in probability and its applications, especially for contributions to the study of Markov processes.

2014 - Wald Memorial Lecturer

2005 - Fellow of the American Academy of Arts and Sciences

- Mathematical analysis
- Statistics
- Stochastic process

His scientific interests lie mostly in Markov chain, Combinatorics, Statistical physics, Markov renewal process and Semigroup. His Markov chain study combines topics from a wide range of disciplines, such as Discrete mathematics, Mathematical optimization and Convergence of random variables. His Statistical physics research includes themes of Stochastic process, Statistics, Measure and Population model.

His Markov renewal process study focuses on Variable-order Markov model and Markov property. His research investigates the connection with Semigroup and areas like Rate function which intersect with concerns in Mathematical analysis. Markov model is frequently linked to Applied mathematics in his study.

- Markov Processes: Characterization and Convergence (4417 citations)
- Solutions of ordinary differential equations as limits of pure jump markov processes (776 citations)
- Limit theorems for sequences of jump Markov processes approximating ordinary differential processes (475 citations)

His primary areas of investigation include Mathematical analysis, Applied mathematics, Markov chain, Markov process and Discrete mathematics. His Mathematical analysis study which covers Martingale that intersects with Uniqueness, Viscosity solution, Conditional probability distribution and Boundary value problem. His Applied mathematics research integrates issues from Stochastic process and Mathematical optimization.

His studies link Statistical physics with Markov chain. His research on Markov process focuses in particular on Markov kernel. His research on Discrete mathematics also deals with topics like

- Combinatorics and related Infinitesimal,
- Lebesgue measure that intertwine with fields like Poisson distribution.

- Mathematical analysis (26.21%)
- Applied mathematics (25.52%)
- Markov chain (24.83%)

- Markov chain (24.83%)
- Statistical physics (18.62%)
- Applied mathematics (25.52%)

Thomas G. Kurtz focuses on Markov chain, Statistical physics, Applied mathematics, Stochastic process and Stochastic modelling. The various areas that he examines in his Markov chain study include Martingale, State and Markov process. His Statistical physics research is multidisciplinary, incorporating elements of Constant, Limit, Scaling and Stationary distribution.

His Limit study incorporates themes from Poisson distribution and Mathematical optimization. His study focuses on the intersection of Applied mathematics and fields such as Boundary value problem with connections in the field of Differential equation and Numerical partial differential equations. His research in Stochastic process tackles topics such as Zero which are related to areas like Brownian motion.

- Continuous Time Markov Chain Models for Chemical Reaction Networks (219 citations)
- Product-form stationary distributions for deficiency zero chemical reaction networks. (156 citations)
- Separation of time-scales and model reduction for stochastic reaction networks. (134 citations)

- Mathematical analysis
- Statistics
- Stochastic process

Thomas G. Kurtz mainly investigates Statistical physics, Markov chain, Stochastic process, Continuous-time Markov chain and Mathematical analysis. He usually deals with Statistical physics and limits it to topics linked to Stochastic modelling and Markov renewal process, Balance equation, Additive Markov chain, Markov chain mixing time and Variable-order Markov model. His Markov chain research incorporates elements of Scaling and Applied mathematics.

Thomas G. Kurtz combines subjects such as Mathematics education, Discretization, Order of accuracy and Tau-leaping with his study of Applied mathematics. His biological study spans a wide range of topics, including Zero and Stationary distribution. His studies in Mathematical analysis integrate themes in fields like Local martingale and Pure mathematics.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Markov Processes: Characterization and Convergence

Stewart N. Ethier;Thomas G. Kurtz.

**(1986)**

8401 Citations

Solutions of ordinary differential equations as limits of pure jump markov processes

Thomas G. Kurtz.

Journal of Applied Probability **(1970)**

1067 Citations

Approximation of Population Processes

Thomas G. Kurtz.

**(1987)**

723 Citations

Weak Limit Theorems for Stochastic Integrals and Stochastic Differential Equations

Thomas G. Kurtz;Philip Protter.

Annals of Probability **(1991)**

699 Citations

Limit theorems for sequences of jump Markov processes approximating ordinary differential processes

T. G. Kurtz.

Journal of Applied Probability **(1971)**

692 Citations

Strong approximation theorems for density dependent Markov chains

Thomas G. Kurtz.

Stochastic Processes and their Applications **(1978)**

635 Citations

The Relationship between Stochastic and Deterministic Models for Chemical Reactions

Thomas G. Kurtz.

Journal of Chemical Physics **(1972)**

629 Citations

The changing nature of network traffic: scaling phenomena

A. Feldmann;A. C. Gilbert;W. Willinger;T. G. Kurtz.

acm special interest group on data communication **(1998)**

513 Citations

Large Deviations for Stochastic Processes

Jin Feng;Thomas G. Kurtz.

**(2006)**

475 Citations

Particle Representations for Measure-Valued Population Models

Peter Donnelly;Thomas G. Kurtz.

Annals of Probability **(1999)**

313 Citations

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