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Inference The basic use of a graphical model is to perform inference: making predictions about the values of unobserved variables, conditioned on the values of observed variables and the parameters. FACTORIE has implementations of many common inference algorithms based on both belief propagation and MCMC. Tutorial » Learning Through a modular specification of inference, optimization, and learning
Numerical Optimization: Understanding L-BFGS — aria42
Numerical Optimization: Understanding L-BFGS Numerical optimization is at the core of much of machine learning. Once you’ve defined your model and have a dataset ready, estimating the parameters of your model typically boils down to minimizing some multivariate function $f(x)$, where the input $x$ is in some high-dimensional space and corresponds to model parameters. In other words, if you solve:
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https://people.eecs.berkeley.edu/~elghaoui/Teaching/EE227A/lecture1.pdf
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