26,675 research outputs found
An efficient message passing algorithm for multi-target tracking
We propose a new approach for multi-sensor multi-target tracking by constructing statistical models on graphs with continuous-valued nodes for target states and discrete-valued nodes for data association hypotheses. These graphical representations lead to message-passing algorithms for the fusion of data across time, sensor, and target that are radically different than algorithms such as those found in state-of-the-art multiple hypothesis tracking (MHT) algorithms. Important differences include: (a) our message-passing algorithms explicitly compute different probabilities and estimates than MHT algorithms; (b) our algorithms propagate information from future data about past hypotheses via messages backward in time (rather than doing this via extending track hypothesis trees forward in time); and (c) the combinatorial complexity of the problem is manifested in a different way, one in which particle-like, approximated, messages are propagated forward and backward in time (rather than hypotheses being enumerated and truncated over time). A side benefit of this structure is that it automatically provides smoothed target trajectories using future data. A major advantage is the potential for low-order polynomial (and linear in some cases) dependency on the length of the tracking interval N, in contrast with the exponential complexity in N for so-called N-scan algorithms. We provide experimental results that support this potential. As a result, we can afford to use longer tracking intervals, allowing us to incorporate out-of-sequence data seamlessly and to conduct track-stitching when future data provide evidence that disambiguates tracks well into the past
De novo construction of polyploid linkage maps using discrete graphical models
Linkage maps are used to identify the location of genes responsible for
traits and diseases. New sequencing techniques have created opportunities to
substantially increase the density of genetic markers. Such revolutionary
advances in technology have given rise to new challenges, such as creating
high-density linkage maps. Current multiple testing approaches based on
pairwise recombination fractions are underpowered in the high-dimensional
setting and do not extend easily to polyploid species. We propose to construct
linkage maps using graphical models either via a sparse Gaussian copula or a
nonparanormal skeptic approach. Linkage groups (LGs), typically chromosomes,
and the order of markers in each LG are determined by inferring the conditional
independence relationships among large numbers of markers in the genome.
Through simulations, we illustrate the utility of our map construction method
and compare its performance with other available methods, both when the data
are clean and contain no missing observations and when data contain genotyping
errors and are incomplete. We apply the proposed method to two genotype
datasets: barley and potato from diploid and polypoid populations,
respectively. Our comprehensive map construction method makes full use of the
dosage SNP data to reconstruct linkage map for any bi-parental diploid and
polyploid species. We have implemented the method in the R package netgwas.Comment: 25 pages, 7 figure
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