Commit aef5972c authored by Gilles Kratzer's avatar Gilles Kratzer
Browse files

update website/vignette

parent cce67658
Pipeline #3351 passed with stage
in 4 seconds
......@@ -246,8 +246,8 @@
<p>A list with an entry for the list of sampled DAGs, the list of scores, the acceptance probability, the method used for each MCMC jump, the rejection status for each MCMC jump, the total number of iterations the thinning, the length of burn-in phase, the named list of distribution per node, the heating parameter for each chain and a data.frame with the score of all chains. The returned object is of class mcmcabn.</p>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
<p>For the implementation of the function:</p>
<p>Kratzer, G., Furrer, R. "Is a single unique Bayesian network enough to accurately represent your data?". arXiv preprint arXiv:1902.06641.</p>
<p>For the general methodology:</p>
<p>Kratzer G, Lewis FI, Willi B, Meli ML, Boretti FS, Hofmann-Lehmann R, Torgerson P, Furrer R and Hartnack S (2020) Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland. Front. Vet. Sci. 7:73. doi: 10.3389/fvets.2020.00073.</p>
<p>For the new edge reversal:</p>
<p>Grzegorczyk, M., Husmeier, D. (2008). "Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move", Machine Learning, vol. 71(2-3), 265.</p>
<p>For the Markov Blanket resampling move:</p>
......
......@@ -238,8 +238,10 @@
<p>A list with an entry for the list of sampled DAGs, the list of scores, the acceptance probability, the method used for each MCMC jump, the rejection status for each MCMC jump, the total number of iterations the thinning, the length of burn-in phase, the named list of distribution per node and the heating parameter. The returned object is of class mcmcabn.</p>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
<p>For the implementation of the function:</p>
<p>Kratzer, G., Furrer, R. "Is a single unique Bayesian network enough to accurately represent your data?". arXiv preprint arXiv:1902.06641.</p>
<p>For the implementation of the function:</p>
<p>Kratzer G, Lewis FI, Willi B, Meli ML, Boretti FS, Hofmann-Lehmann R, Torgerson P, Furrer R and Hartnack S (2020) Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland. Front. Vet. Sci. 7:73. doi: 10.3389/fvets.2020.00073.</p>
<p>For the new edge reversal:</p>
<p>Grzegorczyk, M., Husmeier, D. (2008). "Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move", Machine Learning, vol. 71(2-3), 265.</p>
<p>For the Markov Blanket resampling move:</p>
......
......@@ -72,9 +72,9 @@ The parameter \code{heating} could improve convergence. It should be a real posi
\author{Gilles Kratzer}
\references{
For the implementation of the function:
For the general methodology:
Kratzer, G., Furrer, R. "Is a single unique Bayesian network enough to accurately represent your data?". arXiv preprint arXiv:1902.06641.
Kratzer G, Lewis FI, Willi B, Meli ML, Boretti FS, Hofmann-Lehmann R, Torgerson P, Furrer R and Hartnack S (2020) Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland. Front. Vet. Sci. 7:73. doi: 10.3389/fvets.2020.00073.
For the new edge reversal:
......
......@@ -66,9 +66,10 @@ The parameter \code{heating} could improve convergence. It should be a real posi
\author{Gilles Kratzer}
\references{
For the implementation of the function:
Kratzer, G., Furrer, R. "Is a single unique Bayesian network enough to accurately represent your data?". arXiv preprint arXiv:1902.06641.
Kratzer G, Lewis FI, Willi B, Meli ML, Boretti FS, Hofmann-Lehmann R, Torgerson P, Furrer R and Hartnack S (2020) Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland. Front. Vet. Sci. 7:73. doi: 10.3389/fvets.2020.00073.
For the new edge reversal:
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment