Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and Computin

Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs

The authors describe Maximum-Likelihood Continuity Mapping (MALCOM) as an alternative to hidden Markov models (HMMs) for processing sequence data such as speech. While HMMs have a discrete ''hidden'' space constrained by a fixed finite-automata architecture, MALCOM has a continuous hidden space (a continuity map) that is constrained only by a smoothness requirement on paths through the space. MALCOM fits into the same probabilistic framework for speech recognition as HMMs, but it represents a far more realistic model of the speech production process. The authors support this claim by generating continuity maps for three speakers and using the resulting MALCOM paths to predict measured speech articulator data. The correlations between the MALCOM paths (obtained from only the speech acoustics) and the actual articulator movements average 0.77 on an independent test set not used to train MALCOM nor the predictor. On average, this unsupervised model achieves 92% of performance obtained using the corresponding supervised method

Hic-5 Communicates between Focal Adhesions and the Nucleus through Oxidant-Sensitive Nuclear Export Signal

hic-5 was originally isolated as an H(2)O(2)-inducible cDNA clone whose product was normally found at focal adhesions. In this study, we found that Hic-5 accumulated in the nucleus in response to oxidants such as H(2)O(2). Other focal adhesion proteins including paxillin, the most homologous to Hic-5, remained in the cytoplasm. Mutation analyses revealed that the C- and N-terminal halves of Hic-5 contributed to its nuclear localization in a positive and negative manner, respectively. After the finding that leptomycin B (LMB), an inhibitor of nuclear export signal (NES), caused Hic-5 to be retained in the nucleus, Hic-5 was demonstrated to harbor NES in the N-terminal, which was sensitive to oxidants, thereby regulating the nuclear accumulation of Hic-5. NES consisted of a leucine-rich stretch and two cysteines with a limited similarity to Yap/Pap-type NES. In the nucleus, Hic-5 was suggested to participate in the gene expression of c-fos. Using dominant negative mutants, we found that Hic-5 was actually involved in endogenous c-fos gene expression upon H(2)O(2) treatment. Hic-5 was thus proposed as a focal adhesion protein with the novel aspect of shuttling between focal adhesions and the nucleus through an oxidant-sensitive NES, mediating the redox signaling directly to the nucleus