<div dir="ltr">Bom dia a todos,<div><br></div><div>Para os Bayesianos e quasi-Bayesianos interessados inferência a posteriori para o número de fatores comuns em análise fatorial.</div><div><br></div><div>Abs,</div><div>Hedibert</div><div><br><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <strong class="gmail_sendername" dir="auto">BA Editor Mark Steel</strong> <span dir="auto"><<a href="mailto:bajournal@bayesian.org">bajournal@bayesian.org</a>></span><br>Date: Wed, Sep 18, 2024 at 9:30 AM<br>Subject: [Bayesian Analysis] Call for open discussion<br>To: Prof. Hedibert Lopes <<a href="mailto:hedibert@gmail.com">hedibert@gmail.com</a>><br></div><br><br><p style="text-align:start;margin-bottom:11px">Dear all,</p>

<p style="text-align:start;margin-bottom:11px">As you know, Bayesian Analysis regularly publishes papers with discussion. We now welcome <strong>contributed discussions</strong> of the following manuscript: </p>

<p>“<em>Sparse Bayesian factor analysis when the number of factors is unknown</em>”,</p>

<p>by Sylvia Frühwirth-Schnatter, Darjus Hosszejni and Hedibert Freitas Lopes.</p>

<p>The manuscript can be found in the Advance publication section of the journal website or at the following URL: <a href="https://u8042292.ct.sendgrid.net/ls/click?upn=u001.VwavI53V6vcw3NkZZaT5c3BCVF1lIPzEbB-2BolE3kZnK9V48Q6rSXnzm7ACvF-2B-2Fkzu9jFGLr6fu3q4vmb8e2hTpM-2F1gP-2BucWNMZHjUlPz4-2F7y4JO5bv-2FLnfVEOqEkztukWNcR_IKY80bsE1fiG840z8YTQUCOCmnKHn-2B8CkQMsORxMAKkSXHfoOT5qCLNv0B1J2YyPsqAklWU4zj-2BdJTIVeanayzdWkn8de7ZHvvT8MQ-2BIKalqFgUnkEfYXMdo40TXVXgPJw2XPyvEGmwn63hu4fsshBLgjz-2FT2X3UAkHBx1UanIA3jc44ZMuAr5EgJlmJtEy0xMxjsfdK4flJ2jbUclLujd8gz8t6woLU4A5me3wK2Cjkk4huhZ7RYNtJWzTqshRcTI2DL-2Bbu6IXhB8405wXn-2FeBfERUOXmRabl9bEgzGaE9yWSwMh5xD-2FcomKGGF8506" rel="nofollow" target="_blank">https://doi.org/10.1214/24-BA1423</a></p>

<p><strong>Abstract</strong>: <em>There has been increased research interest in the subfield of sparse Bayesian factor analysis with shrinkage priors, which achieve additional sparsity beyond the natural parsimonity of factor models. In this spirit, we estimate the number of common factors in the widely applied sparse latent factor model with spike-and-slab priors on the factor loadings matrix. Our framework leads to a natural, efficient and simultaneous coupling of model estimation and selection on one hand and model identification and rank estimation (number of factors) on the other hand. More precisely, by embedding the unordered generalised lower triangular loadings representation into overfitting sparse factor modelling, we obtain
posterior summaries regarding factor loadings, common factors as well as the factor dimension via postprocessing draws from our efficient and customized Markov chain Monte Carlo scheme.</em></p>

<p>Please note that the contributions should be no more than two pages in length (references not included), using the BA LaTeX style: <a href="https://u8042292.ct.sendgrid.net/ls/click?upn=u001.VwavI53V6vcw3NkZZaT5c3BCVF1lIPzEbB-2BolE3kZnK9V48Q6rSXnzm7ACvF-2B-2Fkzu9jFGLr6fu3q4vmb8e2hTpM-2F1gP-2BucWNMZHjUlPz4-2F4geMY03Mnen-2FhFiByQtrHtZIt5_IKY80bsE1fiG840z8YTQUCOCmnKHn-2B8CkQMsORxMAKkSXHfoOT5qCLNv0B1J2YyPsqAklWU4zj-2BdJTIVeanayzdWkn8de7ZHvvT8MQ-2BIKalqFgUnkEfYXMdo40TXVXgPJw2XPyvEGmwn63hu4fsshBLgjz-2FT2X3UAkHBx1UanIAFb4agquuxBLEF3EHQtZwLaadvJZsk5UlJmA9prTBfNe5Aznq9rQY4YTNHnagw8lIcG8OkPz2jtijW9tx2DhNuKa-2Fc7qkydEIwFJ4q0K27VczDC9LayW-2B-2BFSkKb7tTTjq57OXfnuyEPt14sc9K3V-2Bn" rel="nofollow" target="_blank">LaTeX Author Package for publications in Bayesian Analysis journal (e-publications.org)</a></p>

<p>The discussions should be submitted to the journal using the Electronic Journal Management System (EJMS) submission page (<a href="https://u8042292.ct.sendgrid.net/ls/click?upn=u001.VwavI53V6vcw3NkZZaT5c3BCVF1lIPzEbB-2BolE3kZnK9V48Q6rSXnzm7ACvF-2B-2Fkzu9jFGLr6fu3q4vmb8e2hTpM-2F1gP-2BucWNMZHjUlPz4-2F6JGGMoLi2-2Fi219FHp-2Bnl88OxmA_IKY80bsE1fiG840z8YTQUCOCmnKHn-2B8CkQMsORxMAKkSXHfoOT5qCLNv0B1J2YyPsqAklWU4zj-2BdJTIVeanayzdWkn8de7ZHvvT8MQ-2BIKalqFgUnkEfYXMdo40TXVXgPJw2XPyvEGmwn63hu4fsshBLgjz-2FT2X3UAkHBx1UanIBKeOxi4PmCuGNKJoahoBEZ2J-2BnA4KqurpO79W2H2JqqJ65WhHIqwviY-2FzDyhf7iMa981R6p6Ty2B9ihqUCm8-2Bifmgc7O7Bs-2B7mMYTU8-2Fs6TZvuTf-2Bm6N8lXz41HTzyJE9jQpGHU5fcVxJnfE0F8SO8" rel="nofollow" target="_blank">https://www.e-publications.org/ims/submission/</a>) before <b>October 20, 2024</b>. Please choose “contributed discussion” as manuscript type and clearly indicate that your discussion refers to paper BA1423 when submitting your contribution. </p>

<p>I look forward to seeing many interesting and thoughtful contributions appear in the journal.</p>
<hr>
Sent from  the International Society for Bayesian Analysis <div><span><span><a href="mailto:members-info@bayesian.org" target="_blank">members-info@bayesian.org</a></span><br><span>United States</span></span></div>

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