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Capacity estimation of two-dimensional channels using Sequential Monte Carlo
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
2014 (Engelska)Ingår i: 2014 IEEE Information Theory Workshop, 2014, s. 431-435Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D (1, ∞) run-length limited constrained channel, but the underlying idea is generally applicable. The proposed algorithm is profiled against a state-of-the-art method, yielding more than an order of magnitude improvement in estimation accuracy for a given computation time.

Ort, förlag, år, upplaga, sidor
2014. s. 431-435
Nationell ämneskategori
Reglerteknik Datavetenskap (datalogi) Sannolikhetsteori och statistik
Identifikatorer
URN: urn:nbn:se:liu:diva-112966DOI: 10.1109/ITW.2014.6970868OAI: oai:DiVA.org:liu-112966DiVA, id: diva2:775991
Konferens
Information Theory Workshop
Tillgänglig från: 2015-01-06 Skapad: 2015-01-06 Senast uppdaterad: 2018-11-09
Ingår i avhandling
1. Machine learning using approximate inference: Variational and sequential Monte Carlo methods
Öppna denna publikation i ny flik eller fönster >>Machine learning using approximate inference: Variational and sequential Monte Carlo methods
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubiquitous in our everyday life. The systems we design, and technology we develop, requires us to coherently represent and work with uncertainty in data. Probabilistic models and probabilistic inference gives us a powerful framework for solving this problem. Using this framework, while enticing, results in difficult-to-compute integrals and probabilities when conditioning on the observed data. This means we have a need for approximate inference, methods that solves the problem approximately using a systematic approach. In this thesis we develop new methods for efficient approximate inference in probabilistic models.

There are generally two approaches to approximate inference, variational methods and Monte Carlo methods. In Monte Carlo methods we use a large number of random samples to approximate the integral of interest. With variational methods, on the other hand, we turn the integration problem into that of an optimization problem. We develop algorithms of both types and bridge the gap between them.

First, we present a self-contained tutorial to the popular sequential Monte Carlo (SMC) class of methods. Next, we propose new algorithms and applications based on SMC for approximate inference in probabilistic graphical models. We derive nested sequential Monte Carlo, a new algorithm particularly well suited for inference in a large class of high-dimensional probabilistic models. Then, inspired by similar ideas we derive interacting particle Markov chain Monte Carlo to make use of parallelization to speed up approximate inference for universal probabilistic programming languages. After that, we show how we can make use of the rejection sampling process when generating gamma distributed random variables to speed up variational inference. Finally, we bridge the gap between SMC and variational methods by developing variational sequential Monte Carlo, a new flexible family of variational approximations.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2018. s. 39
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1969
Nationell ämneskategori
Reglerteknik Datavetenskap (datalogi) Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-152647 (URN)10.3384/diss.diva-152647 (DOI)9789176851616 (ISBN)
Disputation
2018-12-14, Ada Lovelace, Building B, Campus Valla, Linköping, 10:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-11-27 Skapad: 2018-11-09 Senast uppdaterad: 2019-09-26Bibliografiskt granskad

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Andersson Naesseth, ChristianLindsten, FredrikSchön, Thomas

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