<p>Panels a-d show the lognormal, gamma, inverse Gaussian, and inverse gamma pdfs respectively, for the independent variance parameter set Ω<sub><i>IV</i></sub> (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124787#sec019" target="_blank">Methods</a>). The ‘preferred’ and ‘null’ density functions (<i>f</i><sub>*</sub>, <i>f</i><sub>0</sub>) are in red and black respectively. The plots are for ISIs from 1 to 100 ms. For infinitesimal ISIs, the lognormal, inverse Gaussian and inverse gamma tend to zero; for the the gamma the pdf grows up to a bound as the ISI tends to zero. Panels e-f are the corresponding contributions <i>L</i><sub><i>i</i></sub>(<i>j</i>) to the accumulated ‘evidence’ <i>y</i><sub><i>i</i></sub>...
<p>To ease visualisation, all panels show the exponential of the thresholds; <i>ϕ</i><sub><i>s</i></...
<p>The dotted lines represent the mean coverages <i>p</i><sub><i>l</i></sub> = .75 and <i>p</i><sub>...
In this paper, we introduce a new and efficient data augmentation approach to the posterior inferenc...
<p>Each panel shows mean decision sample as a function of the number of choices for a range of pdfs ...
<p>The top row shows noise CDFs, , for (A) a Laplace distribution (generalized Gaussian with ), (B) ...
<p>For Gamma, Log-normal and Inverse Gaussian distributions, the notation of parameters, the probabi...
<p>For comparison, the ABC plots for the uniform distribution, <i>U = Uniform [0</i>,<i>m]</i>, wher...
<p>A) Average cTE values for delta-gamma (orange), theta-gamma (green), and alpha-gamma (blue) PAC w...
<p>Each bar shows, for the pdf indicated in the legend, the mean decision sample for <i>N</i> = 10 a...
<p>The left column shows the estimated PDF based on four samples colored as green, red, blue and mag...
<p>Simulated samples from the Gaussian distribution with <i>μ</i> = 0 and <math><mrow><mi>σ</mi><mo>...
<p>The Q-Q plots for genome-wide p-values for phenotype left hippocampus volume from longitudinal an...
<p>The values of the model parameters are: yielding and (upper panels); yielding and (middle p...
<p>A Gaussian has zero excess kurtosis. Here as in Example 2 of the original paper <a href="http://...
<p>Simulated samples from the square Gaussian distribution with <i>γ</i> = 0.07 (top left panel) and...
<p>To ease visualisation, all panels show the exponential of the thresholds; <i>ϕ</i><sub><i>s</i></...
<p>The dotted lines represent the mean coverages <i>p</i><sub><i>l</i></sub> = .75 and <i>p</i><sub>...
In this paper, we introduce a new and efficient data augmentation approach to the posterior inferenc...
<p>Each panel shows mean decision sample as a function of the number of choices for a range of pdfs ...
<p>The top row shows noise CDFs, , for (A) a Laplace distribution (generalized Gaussian with ), (B) ...
<p>For Gamma, Log-normal and Inverse Gaussian distributions, the notation of parameters, the probabi...
<p>For comparison, the ABC plots for the uniform distribution, <i>U = Uniform [0</i>,<i>m]</i>, wher...
<p>A) Average cTE values for delta-gamma (orange), theta-gamma (green), and alpha-gamma (blue) PAC w...
<p>Each bar shows, for the pdf indicated in the legend, the mean decision sample for <i>N</i> = 10 a...
<p>The left column shows the estimated PDF based on four samples colored as green, red, blue and mag...
<p>Simulated samples from the Gaussian distribution with <i>μ</i> = 0 and <math><mrow><mi>σ</mi><mo>...
<p>The Q-Q plots for genome-wide p-values for phenotype left hippocampus volume from longitudinal an...
<p>The values of the model parameters are: yielding and (upper panels); yielding and (middle p...
<p>A Gaussian has zero excess kurtosis. Here as in Example 2 of the original paper <a href="http://...
<p>Simulated samples from the square Gaussian distribution with <i>γ</i> = 0.07 (top left panel) and...
<p>To ease visualisation, all panels show the exponential of the thresholds; <i>ϕ</i><sub><i>s</i></...
<p>The dotted lines represent the mean coverages <i>p</i><sub><i>l</i></sub> = .75 and <i>p</i><sub>...
In this paper, we introduce a new and efficient data augmentation approach to the posterior inferenc...