Efficient sampling for Bayesian inference of conjunctive Bayesian networks

Efficient sampling for Bayesian inference of conjunctive Bayesian networks

MOTIVATION Cancer development is driven by the accumulation of advantageous mutations and subsequent clonal expansion of cells harbouring these mutations, but the order in which mutations occur remains poorly understood. Advances in genome sequencing and the soon-arriving flood of cancer genome data produced by large cancer sequencing consortia hold the promise to elucidate cancer progression. ...

Efficient Nonparametric Importance Sampling for Bayesian Inference

Conjunctive Bayesian Networks

Conjunctive Bayesian networks (CBNs) are graphical models that describe the accumulation of events which are constrained in the order of their occurrence. A CBN is given by a partial order on a (finite) set of events. CBNs generalize the oncogenetic tree models of Desper et al. (1999) by allowing the occurrence of an event to depend on more than one predecessor event. The present paper studies ...

Efficient Inference for Mixed Bayesian Networks

Bayesian network is a compact representation for probabilistic models and inference. They have been used successfully for multisensor fusion and situation assessment. It is well known that, in general, the inference algorithms to compute the exact posterior probability of the target state are either computationally infeasible for dense networks or impossible for mixed discretecontinuous network...

Efficient Stochastic Sampling Algorithms for Bayesian Networks

Graphical models are increasingly popular tools for modeling problems involving uncertainty. They deal with uncertainty by modeling and reasoning about degrees of uncertainty explicitly based on probability theory. Practical models based on graphical models often reach the size of hundreds of variables. Although a number of ingenious inference algorithms have been developed, the problem of exac...