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Peter Young was one of the first scientists to recognise the importance of recursive estimation in the modelling and forecasting of stochastic dynamic systems. He is well known for his work in this area over the past 45 years, as well as research in other areas, such as environmental modelling and automatic control system design. He has evolved and promoted the inductive 'Data-Based Mechanistic' (DBM) approach to modelling uncertain dynamic systems and applied this in various areas of study, including the environment and climate, macro economics, and business applications. His most recent DBM modelling and forecasting research has been concerned with rainfall-flow modelling and real-time flood forecasting, where he worked on a project within the UK Flood Risk Management Research Consortium and published numerous papers, as listed in his publications. These include a recent paper which introduces 'Hypothetico-Inductive DBM' modelling and applies this to well known rainfall-flow data from the Leaf River in the USA. Most recently, he has worked on a unified approach to the optimal estimation of discrete and continuous time transfer function models from sampled data and the data-based mechanistic modelling and forecasting global climate data.
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AUSTRALIAN SOCIAL WORKpp.1-12, (2022)
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Advances in social workno. 1 (2021): 76-89
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