FungalGrowthDataset

<p>Time-series dataset with \(N\times R \times T\) growth levels for \(R = 3\) replicates of \(N = 411\) objects (mutants) measured every 10 minutes for \(T = 289\) time-points. See Papastamoulis et al (2017) for a detailed description.</p>

datasets

Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2017) <arXiv:1709.06111> for a detailed presentation of the method.

FungalGrowthDataset: Fungal Growth Dataset

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