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Dear all,</div>
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Hope you've all been having a nice week. </div>
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Just a reminder of the S3RI seminar scheduled for this afternoon at 2 pm, we have Dr Alexander Shestopaloff (Queen Mary University of London) speaking to us about </div>
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<span style="color:rgb(0,0,0); font-family:Arial,Helvetica,sans-serif; background-color:rgb(255,255,255); display:inline!important">"Scaling Monte Carlo inference for State-Space Models". </span><br>
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Please see below for further details for the talk.</div>
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Hope to see you there later today!</div>
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Kind regards,<br>
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Jessie</div>
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<span style="font-family:Arial,Helvetica,sans-serif; font-size:12pt">Title: Scaling Monte Carlo inference for State-Space Models</span><br>
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<span style="margin:0px; font-size:12pt; font-family:Arial,Helvetica,sans-serif">Alexander Shestopaloff (Queen Mary University of London)</span><br>
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The iterated conditional Sequential Monte Carlo (cSMC) method is a particle MCMC method commonly used for state inference in non-linear, non-Gaussian state space models. Standard implementations of iterated cSMC provide an efficient way to sample state sequences
in low-dimensional state space models. However, efficiently scaling iterated cSMC methods to perform well in models with a high-dimensional state remains a challenge. One reason for this is the use of a global proposal, without reference to the current state
sequence. In high dimensions, such a proposal will typically not be well-matched to the posterior and impede efficient sampling. I will describe a technique based on the embedded HMM (Hidden Markov Model) framework to construct efficient proposals in high
dimensions that are local relative to the current state sequence. A second obstacle to scalability of iterated cSMC is not using the entire observed sequence to construct the proposal. Typical implementations of iterated cSMC use a proposal at time t that
relies only on data up to time t, even when data after time t is available. In high dimensions and in the presence of informative data, such proposals can become inefficient and slow down sampling. I will introduce a principled approach to incorporating all
data in the cSMC proposal at time t. By considering several examples, I will demonstrate that both strategies improve the performance of iterated cSMC for state sequence sampling in high-dimensional state space models.</div>
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</span><span style="margin:0px; font-size:12pt; font-family:Arial,Helvetica,sans-serif; background-color:white">For the current schedule of </span><span style="margin:0px; font-size:12pt; color:rgb(32,31,30); background-color:white"><span class="x_x_markxattfkcqd" style="margin:0px; font-family:Arial,Helvetica,sans-serif; color:rgb(0,0,0)">S3RI</span></span><span style="margin:0px; font-size:12pt; font-family:Arial,Helvetica,sans-serif; background-color:white"> seminars,
see </span><span style="margin:0px; font-size:11.5pt; color:rgb(32,31,30)"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftinyurl.com%2Fs3riseminar&data=04%7C01%7C%7Ca55e60a6e8a44cec238f08d8767bfcc9%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637389622175719905%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=EmpmDyCnZmkqzNMidghKk3%2FF1gI3nkMYe1QvaMaEAY4%3D&reserved=0" originalsrc="https://tinyurl.com/s3riseminar" shash="ZTVBeTKvN5Jh1DkwbnJFMXIgAmIZGmezDb5PH9E38UpFyzolMDNCPIjWaUImk4p75ySJK7yEM2r7cY3ekL8C0ls4sw5KSRd/1mcJZe77/A/adv+7laz3ESt6akKi0bXSLQ71G5ijs4nhEwGQqEZStxmXYwts5QXtcblEidCA+Ig=" originalsrc="https://tinyurl.com/s3riseminar" shash="EwfbWaQSqKDA+TWX+vGAJw+lGHiqf0cFhh4Xoj9uVVoE6nFVuNJoOAgTG62ADbmL3CYEjfGxYLEX4AYrR0myU/uo79FL7iZeHK5M/TpcyJE+2+3NrCW6/FfCsqiwGYlUrxlQIWqX8Prrns0NjZlWosE9nx+M4WhG4KP3XVHV02g=" target="_blank" rel="noopener noreferrer" title="Original URL: https://tinyurl.com/s3riseminar. Click or tap if you trust this link."><span style="margin:0px; font-size:12pt; font-family:Arial,Helvetica,sans-serif; color:rgb(0,0,0)">https://tinyurl.com/</span><span style="margin:0px; font-size:12pt"><span class="x_x_markxattfkcqd" style="margin:0px; font-family:Arial,Helvetica,sans-serif; color:rgb(0,0,0)">s3ri</span></span><span style="margin:0px; font-size:12pt; font-family:Arial,Helvetica,sans-serif; color:rgb(0,0,0)">seminar</span></a><br>
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If you haven't joined yet, but would like to join the </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Arial,Helvetica,sans-serif; color:black; background-color:white"><span style="margin:0px"><span class="x_x_markxattfkcqd" style="margin:0px; font-family:Arial,Helvetica,sans-serif; color:rgb(0,0,0)">S3RI</span></span></span><span style="margin:0px; font-size:12pt; font-family:Arial,Helvetica,sans-serif; color:rgb(0,0,0); background-color:white"> team
on Microsoft Teams please use the link below:</span><span style="margin:0px; font-family:Calibri,Arial,Helvetica,sans-serif"><br>
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<span style="margin:0px; font-size:12pt; font-family:Arial,Helvetica,sans-serif; color:rgb(0,0,0); background-color:white"><a href="https://teams.microsoft.com/l/team/19%3a71f0ed99e3ce45219eef73b9cbd1b0f3%40thread.tacv2/conversations?groupId=260706f2-7819-4bfe-920a-3d55825d8e9c&tenantId=4a5378f9-29f4-4d3e-be89-669d03ada9d8" target="_blank" rel="noopener noreferrer" style="margin:0px">https://teams.microsoft.com/l/team/19%3a71f0ed99e3ce45219eef73b9cbd1b0f3%40thread.tacv2/conversations?groupId=260706f2-7819-4bfe-920a-3d55825d8e9c&tenantId=4a5378f9-29f4-4d3e-be89-669d03ada9d8</a></span></span></span></p>
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