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Latent Force Models with Gaussian Processes

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Dr Neil Lawrence (University of Manchester)
Monday 01 March 2010, 11:00-12:00
TCM Seminar Room, Cavendish Laboratory, Department of Physics.

If you have a question about this talk, please contact Carl Scheffler .

Physics based approaches to data modeling involve constructing an accurate mechanistic model of data, often based on differential equations. Machine learning approaches are typically data driven—-perhaps through regularized function approximation.

These two approaches to data modeling are often seen as polar opposites, but in reality they are two different ends to a spectrum of approaches we might take.

In this talk we introduce latent force models. Latent force models are a new approach to data representation that model data through unknown forcing functions that drive differential equation models. By treating the unknown forcing functions with Gaussian process priors we can create probabilistic models that exhibit particular physical characteristics of interest, for example, in dynamical systems resonance and inertia. This allows us to perform a synthesis of the data driven and physical modeling paradigms. We will show applications of these models in systems biology and modelling of human motion capture data.

This talk is part of the Inference Group series.

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Latent Force Models with Gaussian Processes

📅 Download to calendar (vCal)

👤 Speaker: Dr Neil Lawrence (University of Manchester)
📅 Date & Time: Monday 01 March 2010, 11:00 - 12:00
📍 Venue: TCM Seminar Room, Cavendish Laboratory, Department of Physics

Questions? Contact Carl Scheffler

Abstract

These two approaches to data modeling are often seen as polar opposites, but in reality they are two different ends to a spectrum of approaches we might take.

Series This talk is part of the Inference Group series.

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Latent Force Models with Gaussian Processes

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Latent Force Models with Gaussian Processes

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