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Full-Text Articles in Physical Sciences and Mathematics
Trustworthy, Useful Languages For Probabilistic Modeling And Inference, Neil B. Toronto
Trustworthy, Useful Languages For Probabilistic Modeling And Inference, Neil B. Toronto
Theses and Dissertations
The ideals of exact modeling, and of putting off approximations as long as possible, make Bayesian practice both successful and difficult. Languages for modeling probabilistic processes, whose implementations answer questions about them under asserted conditions, promise to ease much of the difficulty. Unfortunately, very few of these languages have mathematical specifications. This makes them difficult to trust: there is no way to distinguish between an implementation error and a feature, and there is no standard by which to prove optimizations correct. Further, because the languages are based on the incomplete theories of probability typically used in Bayesian practice, they place …
Dynamic Bayesian Approaches To The Statistical Calibration Problem, Derick Lorenzo Rivers
Dynamic Bayesian Approaches To The Statistical Calibration Problem, Derick Lorenzo Rivers
Theses and Dissertations
The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used …