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Gunawan, David Dr.

Lecturer

  • Lecturer - School of Mathematics and Applied Stats

Research Overview


  • I have a general and broad interest in Bayesian computations, from both a methodological and an applied perspective. From a methodological perspective, I am interested in posterior simulation methods, such as Markov chain Monte Carlo, Sequential Monte Carlo, particle Markov chain Monte Carlo, Variational approximations, and Approximate Bayesian computation methods. From an applied perspective, I use Bayesian computational methods to solve real-world problems in many areas, such as economic inequality and poverty measurement, health, cognitive psychology, finance, economics, and environmental studies.

Available as Research Supervisor

Available for Collaborative Projects

Selected Publications


Available as Research Supervisor

Advisees


  • Graduate Advising Relationship

    Degree Research Title Advisee
    Doctor of Philosophy (Integrated) Spatial statistical inference from a decision-theoretic viewpoint with application to non-Gaussian environmental data Pearse, Alan
    Doctor of Philosophy Pricing American options under regime-switching model Zheng, Yawen
    Doctor of Philosophy Modeling Climate Change Impacts on Crude Oil Prices Hemasiri Nawarathna, Anjana Wijayawardhana

Keywords


  • Bayesian Estimation | Markov chain Monte Carlo | sequential Monte Carlo | variational Approximation | Approximate Bayesian Computation | Synthetic Likelihood | Hierarchical-statistical-models | Statistics | Intractable likelihood problems | Spatio-Temporal-Statistics | Time series analysis | big data

Full Name


  • David Gunawan

Mailing Address


  • 39C Northfields Avenue

    Wollongong

    NSW

    2522

    Australia

Research Overview


  • I have a general and broad interest in Bayesian computations, from both a methodological and an applied perspective. From a methodological perspective, I am interested in posterior simulation methods, such as Markov chain Monte Carlo, Sequential Monte Carlo, particle Markov chain Monte Carlo, Variational approximations, and Approximate Bayesian computation methods. From an applied perspective, I use Bayesian computational methods to solve real-world problems in many areas, such as economic inequality and poverty measurement, health, cognitive psychology, finance, economics, and environmental studies.

Selected Publications


Advisees


  • Graduate Advising Relationship

    Degree Research Title Advisee
    Doctor of Philosophy (Integrated) Spatial statistical inference from a decision-theoretic viewpoint with application to non-Gaussian environmental data Pearse, Alan
    Doctor of Philosophy Pricing American options under regime-switching model Zheng, Yawen
    Doctor of Philosophy Modeling Climate Change Impacts on Crude Oil Prices Hemasiri Nawarathna, Anjana Wijayawardhana

Keywords


  • Bayesian Estimation | Markov chain Monte Carlo | sequential Monte Carlo | variational Approximation | Approximate Bayesian Computation | Synthetic Likelihood | Hierarchical-statistical-models | Statistics | Intractable likelihood problems | Spatio-Temporal-Statistics | Time series analysis | big data

Full Name


  • David Gunawan

Mailing Address


  • 39C Northfields Avenue

    Wollongong

    NSW

    2522

    Australia

Research Areas

Geographic Focus