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Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm
TLDR
The EM algorithm is shown to provide a slow but sure way of obtaining maximum likelihood estimates of the parameters of interest in compiling a patient record.
Probabilistic Networks and Expert Systems
TLDR
This is a book that will show you even new to old thing, and when you are really dying of probabilistic networks and expert systems, just pick this book; it will be right for you.
Some matrix-variate distribution theory: Notational considerations and a Bayesian application
SUMMARY We introduce and justify a convenient notation for certain matrix-variate distributions which, by its emphasis on the important underlying parameters, and the theory on which it is based,
The Well-Calibrated Bayesian
Abstract Suppose that a forecaster sequentially assigns probabilities to events. He is well calibrated if, for example, of those events to which he assigns a probability 30 percent, the long-run
Independence properties of directed markov fields
TLDR
A criterion for conditional independence of two groups of variables given a third is given and named as the directed, global Markov property and it is argued that this criterion is easy to use, it is sharper than that given by Kiiveri, Speed, and Carlin and equivalent to that of Pearl.
Causal Inference without Counterfactuals
Abstract A popular approach to the framing and answering of causal questions relies on the idea of counterfactuals: Outcomes that would have been observed had the world developed differently; for
Influence Diagrams for Causal Modelling and Inference
Summary We consider a variety of ways in which probabilistic and causal models can be represented in graphical form. By adding nodes to our graphs to represent parameters, decisions, etc., we obtain
Bayesian analysis in expert systems
TLDR
Using a real, moderately complex, medical example, it is illustrated how qualitative and quantitative knowledge can be represented within a directed graphical model, generally known as a belief network in this context.
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