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problemconsider the problem of computing the optimal action for an agent whose utility function we are uncertain about
problemconsider the problem of bayesian learning for a functional causal model c over a set of endogenous variables x
problem1 consider a particular parameterization theta eta to max-margin show how we can use second-best map inference
problemin this exercise we show how to learn markov networks with shared parameters such as a relational markov network
problemwe now consider how to use the interpretation of the em as maximizing an energy functional to allow partial or
problemsuppose that we have an incomplete data set d and network structure g and matching parameters moreover suppose
problemconsider learning the parameters of the network h rarr x h rarr y where h is a hidden variable show that the
problema consider the task of estimating the parameters of a univariate gaussian distribution n micro sigma2nbsp from a
problemconsider the problem of applying em to parameter estimation for a variable x whose local probabilistic model is
problemsuppose we have a given model px theta on a set of variable x x1 xn and some incomplete data suppose we
problemit is often useful when learning the structure of a bayesian network to consider more global search operations
problemconsider the problem of learning the structure of a 2-tbn over x x1 xn assume that we are learning a model
problemrecall that the thetafn denotes both an asymptotic lower bound and an asymptotic upper bound up to a constant
problemthis problem considers the performance of various types of structure search algorithms suppose we have a general
problemconsider the problem of learning a bayesian network structure over two random variables x and ya show a data set
problemwe now examine how to prove score equivalence for the bde score assume that we have a prior specified by an
problemconsider again the build-pdag procedure of algorithm but now assume that we apply it in a setting where the
problema write down the probabilistic model for the gaussian slam problem with k landmarksb derive the equations for a
problemone of the problems with the particle filtering algorithm is that it uses only the observations obtained so far
problem1 consider a fully persistent dbn over n state variables x show that any clique tree over xt xt1nbspthat we can
problemshow how to combine the ep-based algorithms for clgs and for nonlinear cpds to address clgs where discrete
problemin some cases it is possible to decompose a nonlinear dependency y fx into finer-grained dependencies for
problemshow how the optimal alpha-beta swap step can be found by running min-cut on an appropriately constructed graph
problemconstruct an example of a max-product calibrated cluster graph in which at least some beliefs have two locally