Paul Rozdeba

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Data assimilation transfers information from an observed system to a physically based model system with state variables x(t). The observations are typically noisy, the model has errors, and the initial state x(t0) is uncertain: the data assimilation is statistical. One can ask about expected values of functions 〈G(X)〉 on the path X={x(t0), . . .,x(tm)} of(More)
Pityriasis lichenoides et varioliformis acuta (PLEVA), or Mucha-Habermann disease (MHD), is a cutaneous disorder evident with crops of erythematous macules and papules, usually on the trunk and flexural areas of the extremities. Its etiology remains unknown. PLEVA is speculated to be an inflammatory reaction triggered by certain infectious agents, an(More)
Localized herpes zoster following intra-articular corticosteroid injection is remarkable. We describe an 80-year-old woman with severe osteoarthritis that received an intra-articular injection of 80 mg methylprednisolone in her knee, followed 1 day later by the appearance of linear unilateral vesicles and bullae on her leg in a dermatomal distribution(More)
In statistical data assimilation one evaluates the conditional expected values, conditioned on measurements, of interesting quantities on the path of a model through observation and prediction windows. This often requires working with very high dimensional integrals in the discrete time descriptions of the observations and model dynamics, which become(More)
We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic(More)
We formulate a strong equivalence between machine learning, artificial intelligence methods and the formulation of statistical data assimilation as used widely in physical and biological sciences. The correspondence is that layer number in the artificial network setting is the analog of time in the data assimilation setting. Within the discussion of this(More)
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