In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
We introduce a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The advantage is that we only require a prior distribution on a class of simulators. This is useful when a probabilistic model of the underlying process is too complex to formulate, but where detailed simulation models are available. ABC-RL allows the use of any Bayesian reinforcement learning technique in this case. It can be seen as an extension of simulation methods to both planning and inference. We experimentally demonstrate the potential of this approach in a comparison with LSPI. Finally, we introduce a theorem showing that ABC is sound.

Ex Output:
ABC Reinforcement Learning


Ex Input:
Marginal MAP inference involves making MAP predictions in systems defined<lb>with latent variables or missing information. It is significantly more difficult than<lb>pure marginalization and MAP tasks, for which a large class of efficient and con-<lb>vergent variational algorithms, such as dual decomposition, exist. In this work, we<lb>generalize dual decomposition to a generic power sum inference task, which in-<lb>cludes marginal MAP, along with pure marginalization and MAP, as special cases.<lb>Our method is based on a block coordinate descent algorithm on a new convex<lb>decomposition bound, that is guaranteed to converge monotonically, and can be<lb>parallelized efficiently. We demonstrate our approach on marginal MAP queries<lb>defined on real-world problems from the UAI approximate inference challenge,<lb>showing that our framework is faster and more reliable than previous methods.

Ex Output:
Decomposition Bounds for Marginal MAP


Ex Input:
Currently, criminal’s profile (CP) is obtained from investigator’s or forensic psychologist’s interpretation, linking crime scene characteristics and an offender’s behavior to his or her characteristics and psychological profile. This paper seeks an efficient and systematic discovery of non-obvious and valuable patterns between variables from a large database of solved cases via a probabilistic network (PN) modeling approach. The PN structure can be used to extract behavioral patterns and to gain insight into what factors influence these behaviors. Thus, when a new case is being investigated and the profile variables are unknown because the offender has yet to be identified, the observed crime scene variables are used to infer the unknown variables based on their connections in the structure and the corresponding numerical (probabilistic) weights. The objective is to produce a more systematic and empirical approach to profiling, and to use the resulting PN model as a decision tool. Keywords-component; Modeling, criminal profiling, criminal behavior, probabilistic network, Bayes Rule

Ex Output:

Modeling of Human Criminal Behavior using Probabilistic Networks