<p>The likelihood function is depicted for the parameter <i>y</i>. The solid curve represents the marginal posterior distribution. We used the uniform distribution as a prior probability. The likelihood function was calculated from the data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157773#pone.0157773.t003" target="_blank">Table 3</a>.</p
Solid lines represent the distributions of posterior probabilities for each category and task in the...
<p>Bayesian estimates are shown as hollow squares with error bars showing standard deviations. Estim...
The posterior predictive distribution is the distribution of future observations, conditioned on the...
<p>The posterior distributions of for = 1, …, 5 SNP, respectively, under the Bayesian unconditiona...
Given a probabilistic model Y ∼ `(y|x), x ∈ X where `(y|x) denotes a parameterized density known as ...
Computing marginal probabilities is an important and fundamental issue in Bayesian inference. We pre...
[[abstract]]Computing marginal probabilities is an important and fundamental issue in Bayesian infer...
In Bayesian inference, the posterior distribution for parameters θ ∈ Θ is given by pi(θ|y) ∝ pi(y|θ)...
<p>Bayesian posterior distributions for all of the parameters included in the ultimate model with va...
Slope parameters with 95% Bayesian credible intervals that do not cross zero suggest relationships f...
<p>We plot the probability to report a correct answer <i>A</i> as a function of <i>n</i><sub><i>A</i...
We address the statistical problem of evaluating R = P(X < Y ), where X and Y are two independent ra...
Fitting parameters of interest in an elegant and efficient way via analysis of experimental data is ...
[[sponsorship]]統計科學研究所[[note]]已出版;[SCI];具代表性[[note]]http://gateway.isiknowledge.com/gateway/Gateway....
<p>Probability densities for each of the parameters described in <a href="http://www.plosbiology.org...
Solid lines represent the distributions of posterior probabilities for each category and task in the...
<p>Bayesian estimates are shown as hollow squares with error bars showing standard deviations. Estim...
The posterior predictive distribution is the distribution of future observations, conditioned on the...
<p>The posterior distributions of for = 1, …, 5 SNP, respectively, under the Bayesian unconditiona...
Given a probabilistic model Y ∼ `(y|x), x ∈ X where `(y|x) denotes a parameterized density known as ...
Computing marginal probabilities is an important and fundamental issue in Bayesian inference. We pre...
[[abstract]]Computing marginal probabilities is an important and fundamental issue in Bayesian infer...
In Bayesian inference, the posterior distribution for parameters θ ∈ Θ is given by pi(θ|y) ∝ pi(y|θ)...
<p>Bayesian posterior distributions for all of the parameters included in the ultimate model with va...
Slope parameters with 95% Bayesian credible intervals that do not cross zero suggest relationships f...
<p>We plot the probability to report a correct answer <i>A</i> as a function of <i>n</i><sub><i>A</i...
We address the statistical problem of evaluating R = P(X < Y ), where X and Y are two independent ra...
Fitting parameters of interest in an elegant and efficient way via analysis of experimental data is ...
[[sponsorship]]統計科學研究所[[note]]已出版;[SCI];具代表性[[note]]http://gateway.isiknowledge.com/gateway/Gateway....
<p>Probability densities for each of the parameters described in <a href="http://www.plosbiology.org...
Solid lines represent the distributions of posterior probabilities for each category and task in the...
<p>Bayesian estimates are shown as hollow squares with error bars showing standard deviations. Estim...
The posterior predictive distribution is the distribution of future observations, conditioned on the...