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<p>Dear all,</p>
<p>On Thursday (25 October) at 2pm in <span class="event-where">54
/ 7035 (7B), we have an S3RI seminar from </span><span
class="event-where">Claire Gormley (University College Dublin)
on </span><span class="event-where"><span
class="event-description">"</span></span><span
class="event-where"><span class="event-description"><span
class="event-description"><span class="ynRLnc"></span>Infinite
Mixtures of Infinite Factor Analysers</span>". Details are
given below.<br>
</span></span></p>
<p><span class="event-where"><span class="event-description"><span><span
class="event-description">The seminar will also be
available via a live web-cast at</span></span></span></span><br>
<a
href="https://www.google.com/url?q=https%3A%2F%2Fcoursecast.soton.ac.uk%2FPanopto%2FPages%2FViewer.aspx%3Fid%3D8cccbf5a-af48-4e7d-a91f-4233a3189d03&sa=D&usd=2&usg=AFQjCNHmWfm6CiNrusYdIiS4JgbDp6qSbA"
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</p>
<p><span class="event-where"><span class="event-description"><span><span
class="event-description">The talk will be followed by tea
and cake in the staff reading room on level 4 of building
54. </span></span></span></span></p>
<p><span class="event-where"><span class="event-description"><span><span
class="event-description">All are welcome!</span></span></span></span></p>
<p><span class="event-where"><span class="event-description"><span>Best
wishes,</span></span></span></p>
<p><span class="event-where"><span class="event-description"><span>Helen</span></span></span></p>
<p>Infinite Mixtures of Infinite Factor Analysers (IMIFA) <br>
</p>
<p>Claire Gormley, University College Dublin <br>
</p>
<p>Factor-analytic Gaussian mixture models are often employed as a
model-based approach to clustering high-dimensional data.
Typically, the numbers of clusters and latent factors must be
specified in advance of model fitting, and the optimal pair
selected using a model choice criterion. For computational
reasons, models in which the number of latent factors is common
across clusters are generally considered. <br>
</p>
<p>Here the infinite mixture of infinite factor analysers (IMIFA)
model is introduced. IMIFA employs a Poisson-Dirichlet process
prior to facilitate automatic inference on the number of clusters.
Further, IMIFA employs shrinkage priors to allow cluster specific
numbers of factors, automatically inferred via an adaptive Gibbs
sampler. IMIFA is presented as the flagship of a family of
factor-analytic mixture models, providing flexible approaches to
clustering high-dimensional data. <br>
</p>
<p>Applications to benchmark and real data sets illustrate the IMIFA
model and its advantageous features: IMIFA obviates the need for
model selection criteria, reduces model search and associated
computational burden, improves clustering performance by allowing
cluster-specific numbers of factors, and quantifies uncertainty in
the numbers of clusters and cluster-specific factors.
The IMIFA R package, available on CRAN, facilitates implementation
of our method. <br>
</p>
<p>This is joint work with Keefe Murphy (University College Dublin)
and Cinzia Viroli (Universita di Bologna)</p>
<span class="event-where"><span class="event-description"><span><span
class="event-description"><span> <span
class="event-description"> </span><span
class="event-description"><span
class="event-description">For the current schedule of
S3RI seminars, see <a class="moz-txt-link-freetext"
href="https://tinyurl.com/s3riseminar">https://tinyurl.com/s3riseminar</a>
</span></span></span></span></span></span></span><br>
<p><span class="event-where"><span class="event-description"><span><span
class="event-description"></span></span></span></span></p>
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