<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/a-book-on-rstan-in-japanese-bayesian-statistical-modeling-using-stan-and-r-wonderful-r-volume-2/"></div></div><div><a data-url="https://www.r-bloggers.com/a-book-on-rstan-in-japanese-bayesian-statistical-modeling-using-stan-and-r-wonderful-r-volume-2/" data-counturl="https://www.r-bloggers.com/a-book-on-rstan-in-japanese-bayesian-statistical-modeling-using-stan-and-r-wonderful-r-volume-2/" data-count="horizontal" data-text="A book on RStan in Japanese: Bayesian Statistical Modeling Using Stan and R (Wonderful R, Volume 2)" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://andrewgelman.com/2016/09/26/rstan-book-in-japanese-wonderful-r/"> R – Statistical Modeling, Causal Inference, and Social Science</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><img alt="Bayesian Statistical Modeling Using Stan and R Book Cover" src="https://i1.wp.com/andrewgelman.com/wp-content/uploads/2016/09/wonderful-r-2.jpg?w=150" data-recalc-dims="1"><br>
Wonderful, indeed, to have an RStan book in Japanese:</p>
<ul>
<li>Kentarou Matsuura. 2016. <a target="_blank" href="https://www.amazon.co.jp/gp/product/4320112423/ref=pd_sim_14_1"><i>Bayesian Statistical Modeling Using Stan and R</i></a>. Wonderful R Series, Volume 2. Kyoritsu Shuppan Co., Ltd.
</ul>
<p>Google translate makes the following of the description posted on Amazon Japan (linked from the title above):</p>
<blockquote><p>
In recent years, understanding of the phenomenon by fitting a mathematical model using a probability distribution on data and prompts the prediction “statistical modeling” has attracted attention. Advantage when compared with the existing approach is both of the goodness of the interpretation of the ease and predictability. Since interpretation is likely to easily connect to the next action after estimating the values ​​in the model. It is rated as very effective technique for data analysis Therefore reality. </p>
<p>In the background, the improvement of the calculation speed of the computer, that the large scale of data becomes readily available, there are advances in stochastic programming language to very simple trial and error of modeling. From among these languages, in this document to introduce Stan is a free software. Stan is a package which is advancing rapidly the development equipped with a superior algorithm, it can easily be used from R because the package for R RStan has been published in parallel. Descriptive power of Stan is high, the hierarchical model and state space model can be written in as little as 30 lines, estimated calculation is also carried out automatically. Further tailor-made extensions according to the analyst of the problem is the easily possible. </p>
<p>In general, dealing with the Bayesian statistics books or not to remain in rudimentary content, what is often difficult application to esoteric formulas many real problem. However, this book is a clear distinction between these books, and finished to a very practical content put the reality of the data analysis in mind. The concept of statistical modeling was wearing through the Stan and R in this document, even if the change is grammar of Stan, even when dealing with other statistical modeling tools, I’m sure a great help.
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<p>I’d be happy to replace this with a proper translation if there’s a Japanese speaker out there with some free time (Masanao Yajima translated the citation for us).</p>
<p><b>Big in Japan?</b></p>
<p>I’d like to say Stan’s <a target="_blank" href="https://en.wikipedia.org/wiki/Big_in_Japan_(phrase)">big in Japan</a>, but that idiom implies it’s not so big elsewhere. I can say there’s a very active Twitter community tweeting about Stan in Japanese, which we follow occasionally using Google Translate.</p>
<p>The post <a target="_blank" rel="nofollow" href="http://andrewgelman.com/2016/09/26/rstan-book-in-japanese-wonderful-r/">A book on RStan in Japanese: <i>Bayesian Statistical Modeling Using Stan and R</i> (Wonderful R, Volume 2)</a> appeared first on <a target="_blank" rel="nofollow" href="http://andrewgelman.com/">Statistical Modeling, Causal Inference, and Social Science</a>.</p>
<p><div><div><div><div data-width="55" data-show-faces="false" data-send="true" data-layout="box_count" data-height="62" data-href="https://www.r-bloggers.com/a-book-on-rstan-in-japanese-bayesian-statistical-modeling-using-stan-and-r-wonderful-r-volume-2/"></div></div><div><a data-url="https://www.r-bloggers.com/a-book-on-rstan-in-japanese-bayesian-statistical-modeling-using-stan-and-r-wonderful-r-volume-2/" data-counturl="https://www.r-bloggers.com/a-book-on-rstan-in-japanese-bayesian-statistical-modeling-using-stan-and-r-wonderful-r-volume-2/" data-count="vertical" data-text="A book on RStan in Japanese: Bayesian Statistical Modeling Using Stan and R (Wonderful R, Volume 2)" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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