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Canberra Branch Meeting -- Bayesian Inference for Construction of Inverse Models from Data

  • 10 Oct 2023
  • 5:45 PM - 7:00 PM (AEDT)
  • In-person and online via Zoom

SSA Canberra invites you to its September (technically first October) branch meeting of 2023, which is joint with the ABS, and will feature Robert Niven from UNSW Canberra present on the topic of "Bayesian Inference for Construction of Inverse Models from Data"

Time: Start at 5:45pm and finish by 7:00pm Canberra time.


In-Person: The Gallery, Level 2, Cultural Centre, University Ave, Kambri ANU (The Gallery - Kambri). There is a single level, underground car park at Kambri, with elevator access directly into The Gallery, and two pedestrian access points (Car Parking Kambri | Australian National University | Care Park).


Dinner: After the talk we will be holding a dinner at 7.15pm at Lemon Grass Thai Restaurant, 65 London Circuit, Canberra (Home ( 

If you are interested in attending the dinner, please let us know by 5pm Monday 9 October by entering your details at SSA Canberra Branch dinner attendance sheet or contacting Warren Muller ( ; 0407 916 868). Please regard this as a firm commitment, not just an intention. For withdrawals after the deadline, please remove your name from the sheet and phone or text Warren (0407 916 868).

NOTE: We are offering discounts to SSA early career and student members who attend dinner! For this meeting, dinners will be a fixed charge of $10 for student members and $20 for early career members.  

Talk details

Speaker: A/Prof Robert K. Niven, School of Engineering and Technology, The University of New South Wales, Canberra, Australia.

Topic:  Bayesian Inference for Construction of Inverse Models from Data

AbstractThis talk considers the inverse problem y =f(x), where x and y are observable parameters, in which we wish to recover the model f. Examples include dynamical systems or combat models with y=dx/dt and x=parameter(s), water catchments with y=streamflow and x=rainfall, and groundwater vulnerability with y=pollutant concentration(s) and x=hydrological parameter(s). Historically, these have been solved by many methods, including regression or sparse regularization for dynamical system models, and various empirical correlation methods for rainfall-runoff and groundwater vulnerability models. These can instead be analyzed within a Bayesian framework, using the maximum a posteriori (MAP) method to estimate the model parameters, and the Bayesian posterior distribution to estimate the parameter variances (uncertainty quantification). For systems with unknown covariance parameters, the joint maximum a-posteriori (JMAP) and variational Bayesian approximation (VBA) methods can be used for their estimation. In this seminar, the Bayesian theoretical foundations are first explained, and the method is then demonstrated for a number of dynamical and hydrological systems.  

BiographyA/Prof Robert K. Niven is an academic in engineering at UNSW Canberra, Australia, with research interests in probabilistic inference, theoretical fluid mechanics and environmental contaminants. His work has been recognised by international fellowships, including Churchill, Fulbright, Japanese, Marie Curie and Endeavour fellowships, and substantial research grants. He is Editorial Board member of journals Entropy and Stat, and has led or played major roles in conference organisation (including AFMC, MODSIM and MaxEnt series of conferences). He is an elected Council member of the Australasian Fluid Mechanics Society, and is Lead Chair of the Australasian Fluid Mechanics Conference (AFMC2024) to be held in Canberra, Australia, in December 2024.

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