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Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is
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back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968
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to studying learners’ engagement with feedback have relied heavily on self-reported data. While informative, self-reported data can be susceptible to bias, poor memory, and incorrect self-assessment
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and communication skills in healthcare. You will use sensor-technology to capture multimodal ‘trace’ data including gestures, speech, workspace spatial layout and manual handling of objects. You will
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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will have the opportunity to interact with gravitational-wave researchers throughout Australia and around the world. Students in my group use data from the Laser Interferometer Gravitational-wave
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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their uncertainty to different stakeholders, and evaluate the effect of the conveyed information. The expected outcome of this project is an innovative conversational agent that helps users (e.g
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Understanding factors related to student retention and experience in physics and astrophysics major units. Using quantitative (surveys) and qualitative data (interviews with students) this project
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also provides additional accommodation support to students through a Preferred Accommodation Provider Program and the delivery of information and resources on the private rental market for the broader