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| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f2ea7e; text-align:left; color:#000; padding:0.2em 0.4em;">T24 Introduction to Bayesian Filtering and Smoothing</h2>
 
| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f2ea7e; text-align:left; color:#000; padding:0.2em 0.4em;">T24 Introduction to Bayesian Filtering and Smoothing</h2>
 
|-
 
|-
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| style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">
+
| style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">'''Length:''' 3 hours
−
<!-- '''Intended Audience:'''
+
  
−
'''Description:'''  
+
'''Intended Audience:''' Scientists,
−
      -->
+
engineers,
−
'''Presenter:''' Simo Särkkä
+
and
 +
PhD
 +
students
 +
interested
 +
in
 +
non-­‐linear
 +
filters,
 +
smoothers,
 +
and
 +
their
 +
applications.
 +
Prerequisites
 +
are
 +
basics
 +
of
 +
Bayesian
 +
inference,
 +
multivariate
 +
calculus
 +
and
 +
matrix
 +
algebra.
 +
 
 +
'''Description:''' The
 +
tutorial
 +
is
 +
based
 +
on
 +
the
 +
book
 +
"Bayesian
 +
Filtering
 +
and
 +
Smoothing"
 +
by
 +
the
 +
speaker.
 +
The
 +
tutorial
 +
introduces
 +
the
 +
current
 +
state-­‐of-­‐the-­‐art
 +
of
 +
non-­‐linear
 +
(single-­‐target)
 +
optimal
 +
filtering
 +
and
 +
smoothing
 +
methods
 +
in
 +
a
 +
unified
 +
Bayesian
 +
framework.
 +
The
 +
attendees
 +
learn
 +
what
 +
non-­‐linear
 +
Kalman
 +
filters
 +
and
 +
particle
 +
filters
 +
are,
 +
how
 +
they
 +
are
 +
related,
 +
and
 +
their
 +
relative
 +
advantages
 +
and
 +
disadvantages.
 +
They
 +
also
 +
discover
 +
how
 +
Bayesian
 +
parameter
 +
estimation
 +
methods
 +
can
 +
be
 +
combined
 +
with
 +
the
 +
filtering
 +
and
 +
smoothing
 +
algorithms.
 +
 
 +
Contents
 +
include
 +
statistical
 +
modeling
 +
and
 +
estimation
 +
of
 +
non-­‐linear
 +
and
 +
non-­‐Gaussian
 +
systems,
 +
Bayesian
 +
filtering
 +
and
 +
smoothing
 +
theory,
 +
extended
 +
Kalman
 +
filtering
 +
and
 +
smoothing,
 +
sigma-­‐point
 +
and
 +
unscented
 +
filtering
 +
and
 +
smoothing,
 +
sequential
 +
Monte
 +
Carlo
 +
particle
 +
filtering
 +
and
 +
smoothing,
 +
and
 +
estimation
 +
of
 +
system
 +
parameters.
 +
Example
 +
applications
 +
from
 +
navigation,
 +
remote
 +
surveillance,
 +
and
 +
time
 +
series
 +
analysis.
 +
 
 +
'''Presenter:''' [mailto:simo.sarkka@aalto.fi Simo Särkkä]
 +
 
 +
'''Simo Särkkä'''
 +
is
 +
an
 +
Associate
 +
Professor
 +
and
 +
Academy
 +
Research
 +
Fellow
 +
with
 +
Aalto
 +
University,
 +
Finland,
 +
and
 +
Technical
 +
Advisor
 +
and
 +
Director
 +
of
 +
IndoorAtlas
 +
Ltd.
 +
He
 +
received
 +
his
 +
M.Sc.
 +
and
 +
D.Sc.
 +
degrees
 +
from
 +
Helsinki
 +
University
 +
of
 +
Technology,
 +
Finland,
 +
in
 +
2000
 +
and
 +
2006,
 +
respectively.
 +
In
 +
2013
 +
he
 +
was
 +
a
 +
Visiting
 +
Professor
 +
with
 +
Oxford
 +
University
 +
and
 +
in
 +
2011
 +
Visiting
 +
Scholar
 +
with
 +
University
 +
of
 +
Cambridge.
 +
From
 +
2000
 +
to
 +
2010
 +
he
 +
worked
 +
with
 +
Nokia
 +
Ltd.,
 +
Indagon
 +
Ltd.,
 +
and
 +
Nalco
 +
Company,
 +
and
 +
from
 +
2010
 +
to
 +
2014
 +
as
 +
Senior
 +
Researcher
 +
with
 +
Aalto
 +
University.
 +
He
 +
has
 +
authored
 +
or
 +
coauthored
 +
seventy
 +
scientific
 +
articles
 +
and
 +
has
 +
three
 +
granted
 +
patents.
 +
His
 +
research
 +
interests
 +
are
 +
in
 +
multi-­‐sensor
 +
data
 +
processing
 +
systems
 +
with
 +
applications
 +
in
 +
location
 +
sensing,
 +
machine
 +
learning,
 +
and
 +
medical
 +
technology.
 +
His
 +
book
 +
"Bayesian
 +
Filtering
 +
and
 +
Smoothing"
 +
was
 +
recently
 +
published
 +
via
 +
the
 +
Cambridge
 +
University
 +
Press.
 +
He
 +
is
 +
a
 +
Senior
 +
Member
 +
of
 +
IEEE.
 +
<div align="right">
 +
[[Tutorials| Back to Tutorials]]
 +
</div>
 
</div>
 
</div>
 
|-
 
|-

Revision as of 13:52, 24 February 2016

T24 Introduction to Bayesian Filtering and Smoothing

Length: 3 hours

Intended Audience: Scientists, engineers, and PhD students interested in non-­‐linear filters, smoothers, and their applications. Prerequisites are basics of Bayesian inference, multivariate calculus and matrix algebra.

Description: The tutorial is based on the book "Bayesian Filtering and Smoothing" by the speaker. The tutorial introduces the current state-­‐of-­‐the-­‐art of non-­‐linear (single-­‐target) optimal filtering and smoothing methods in a unified Bayesian framework. The attendees learn what non-­‐linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how Bayesian parameter estimation methods can be combined with the filtering and smoothing algorithms.

Contents include statistical modeling and estimation of non-­‐linear and non-­‐Gaussian systems, Bayesian filtering and smoothing theory, extended Kalman filtering and smoothing, sigma-­‐point and unscented filtering and smoothing, sequential Monte Carlo particle filtering and smoothing, and estimation of system parameters. Example applications from navigation, remote surveillance, and time series analysis.

Presenter: Simo Särkkä

Simo Särkkä is an Associate Professor and Academy Research Fellow with Aalto University, Finland, and Technical Advisor and Director of IndoorAtlas Ltd. He received his M.Sc. and D.Sc. degrees from Helsinki University of Technology, Finland, in 2000 and 2006, respectively. In 2013 he was a Visiting Professor with Oxford University and in 2011 Visiting Scholar with University of Cambridge. From 2000 to 2010 he worked with Nokia Ltd., Indagon Ltd., and Nalco Company, and from 2010 to 2014 as Senior Researcher with Aalto University. He has authored or coauthored seventy scientific articles and has three granted patents. His research interests are in multi-­‐sensor data processing systems with applications in location sensing, machine learning, and medical technology. His book "Bayesian Filtering and Smoothing" was recently published via the Cambridge University Press. He is a Senior Member of IEEE.


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