Sentence examples for informative distribution from inspiring English sources

In information theory, the entropy statistic is a standard scoring method that quantifies the information encoded in a given random variable, where higher entropy implies a more informative distribution.

They follow a non informative distribution defined according to Winbugs [50] (e.g. volume I: rats, a normal hierarchical volume) as the inverse of a gamma distribution (0.001, 0.001) for σ2 and a squared standard distribution [0, 1] for σ2T.

Its standard deviation was derived from a non informative distribution which allowed large standard deviation values and thus, the normal distribution could be flat enough to allow a large range of potential values for simulation.

Not very informative prior distributions for the overall mean, and covariate parameters with an informative distribution on σ e are used.

The Maximum Entropy method (Nigam et al., 1999) for model learning is based on the principle that in the absence of prior knowledge, the least informative distribution, i.e. the distribution with the maximum entropy, is preferred.

This makes sense because for the latter prior we specified a highly informative distribution; that is, the variance of the prior distribution is quite small reflecting strong prior beliefs.

We rely on subject-specific information to derive informative distributions for π, as shown in the sensitivity analysis.

However, if more informative distributions can be safely assumed, the algorithm will exhibit a different behavior leading to improved results.

In other words, individual-weighting results are less dispersed than equal weighting results, and hence provide more informative distributions, even though 50th percentile values are similar.

Since the entropy can be minimized by choosing the most informative distributions (pleft (theta |hat {mathbf {z}}_{1}^{M}right)), our quantizers would generate the most informative quantized measurements, yielding a good estimation accuracy which will be investigated by conducting extensive experiments in Section 5.

However, the gamma prior used a semi-informative distribution that required the risk to be positive.