Identifying Change-Points in Biological Sequences via Sequential Importance Sampling

Sequential importance sampling of binary sequences

Two sequential methods are described for sampling constrained binary sequences from partial solutions. The backward method computes elimination ideals over finite fields and constructs partial solutions that extend. The forward method uses numerical global optimization to determine which partial solutions extend. The methods are applied to restricted orderings, binary dynamics, and random graphs.

Estimating change-points in biological sequences via the cross-entropy method

The genomes of complex organisms, including the human genome, are known to vary in GC content along their length. That is, they vary in the local proportion of the nucleotides G and C, as opposed to the nucleotides A and T. Changes in GC content are often abrupt, producing well-defined regions. We model DNA sequences as a multiple change-point process in which the sequence is separated into seg...

Testing the Rasch Model via Sequential Importance Sampling

Sequential Importance Sampling for Multiway Tables

We describe an algorithm for the sequential sampling of entries in multiway contingency tables with given constraints. The algorithm can be used for computations in exact conditional inference. To justify the algorithm, a theory relates sampling values at each step to properties of the associated toric ideal using computational commutative algebra. In particular, the property of interval cell c...

Moving targets detection using sequential importance sampling

In this paper, we present a new technique for detecting moving targets from image sequences captured by moving sensors. Feature points are detected and tracked through the image sequences. A validity vector is used to describe the consistency of feature trajectories with sensor motion. By using the sequential importance sampling method, an approximation to the posterior distribution of the sens...