Chapter 2 Bayesian learning of latent variable models (2009)
Juha Karhunen, Antti Honkela, Tapani Raiko, Markus Harva, Er Ilin, Matti Tornio, ...
59 60 Bayesian learning of latent variable models 2.1 Bayesian modeling and variational learning: introduction Unsupervised learning methods are often based on a generative approach where the goal is...
Cuevas-Tello, Juan C., Tino, Peter, Raychaudhury, Somak, Yao, Xin, Harva, Markus
We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose and demonstrate an...
BAYESIAN ESTIMATION OF TIME DELAYS BETWEEN UNEVENLY SAMPLED SIGNALS (2008)
A method for estimating time delays between signals that are irregularly sampled is presented. The approach is based on postulating a latent variable model from which the observed signals have been...
Variational inference and learning for continuous-time nonlinear state-space models (2008)
Honkela, Antti, Harva, Markus, Raiko, Tapani, Karhunen, Juha
Inference in continuous-time stochastic dynamical models is a challenging problem. To complement existing sampling-based methods, variational methods have recently been developed for this problem....
Chapter 4 Variational Bayesian learning of generative models (2008)
Juha Karhunen, Antti Honkela, Er Ilin, Tapani Raiko, Markus Harva, Harri Valpola, ...
80 Variational Bayesian learning of generative models 4.1 Bayesian modeling and variational learning: introduction Unsupervised learning methods are often based on a generative approach where the...
Chapter 3 Variational Bayesian learning of generative models (2008)
Harri Valpola, Antti Honkela, Er Ilin, Tapani Raiko, Markus Harva, Tomas Östman, ...
70 Variational Bayesian learning of generative models 3.1 Bayesian modeling and variational learning Unsupervised learning methods are often based on a generative approach where the goal is to find a...
Building Blocks for Variational Bayesian Learning of Latent Variable Models (2007)
Raiko, Tapani, Valpola, Harri, Harva, Markus, Karhunen, Juha
We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, and delay. A large...
and A Kabán, Variational Learning for Rectified Factor Analysis (2007)
Linear factor models with non-negativity constraints have received a great deal of interest in a number of problem domains. In existing approaches, positivity has often been associated with sparsity....
Bayes Blocks: A Python Toolbox for Variational Bayesian Learning (2006)
Honkela, Antti, Harva, Markus, Raiko, Tapani, Valpola, Harri, Karhunen, Juha
Bayes Blocks is a software library implementing variational Bayesian learning of Bayesian networks with rich possibilities for continuous variables. The underlying inference engine has been...
Bayes Blocks: A Python Toolbox for Variational Bayesian Learning (2006)
Honkela, Antti, Harva, Markus, Raiko, Tapani, Valpola, Harri, Karhunen, Juha
Bayes Blocks [1] is a software library implementing variational Bayesian learning of Bayesian networks with rich possibilities for continuous variables [2]. The underlying inference engine has been...
Building Blocks For Variational Bayesian Learning Of Latent Variable Models (2006)
Abteknillinen Korkeakoulu, Tekniska Högskolan, Tapani Raiko, Tapani Raiko, Harri Valpola, ...
We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, and delay. A large...
Building blocks for variational Bayesian learning of latent variable models (2006)
Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen
We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, and delay. A large...
Bayes Blocks: A Python Toolbox for Variational Bayesian Learning ∗ (2006)
Antti Honkela, Markus Harva, Tapani Raiko, Harri Valpola, Juha Karhunen
Bayes Blocks [1] is a software library implementing variational Bayesian learning of Bayesian networks with rich possibilities for continuous variables [2]. The underlying inference engine has been...
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework (2005)
Harva, Markus, Raiko, Tapani, Honkela, Antti, Valpola, Harri, Karhunen, Juha
A software library for constructing and learning probabilistic models is presented. The library offers a set of building blocks from which a large variety of static and dynamic models can be built....
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework (2005)
Harva, Markus, Raiko, Tapani, Honkela, Antti, Valpola, Harri, Karhunen, Juha
A software library for constructing and learning probabilistic models is presented. The library offers a set of building blocks from which a large variety of static and dynamic models can be built....
Bayes Blocks: An implementation of the variational Bayesian building blocks framework (2005)
Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen
A software library for constructing and learning probabilistic models is presented. The library offers a set of building blocks from which a large variety of static and dynamic models can be built....
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework (2005)
Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen
A software library for constructing and learning probabilistic models is presented. The library o#ers a set of building blocks from which a large variety of static and dynamic models can be built....
A variational Bayesian method for rectified factor analysis (2005)
Abstract — Linear factor models with nonnegativity constraints have received a great deal of interest in a number of problem domains. In existing approaches, positivity has often been associated...
Bayes Blocks: An implementation of the variational Bayesian building blocks framework (2005)
Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen
A software library for constructing and learning probabilistic models is presented. The library offers a set of building blocks from which a large variety of static and dynamic models can be built....
Markus Harva, Tekijä Markus Harva, Osasto Tietotekniikan Osasto, Pääaine Informaatiotekniikka, Sivuaine Perustieteiden Sivuaine, Työn Nimi, ...
Tiivistelmä: Ohjaamattomaan oppimiseen perustuvat kuvasekvenssien mallit tuottavat yleensä yksinkertaisia piirteitä kuten reunasuotimia. Nämä yksinkertaiset piirteet eivät tarjoa kovinkaan...
Hierarchical models of variance sources (2003)
Harri Valpola, Markus Harva, Juha Karhunen
In many models, variances are assumed to be constant although this assumption is often unrealistic in practice. Joint modelling of means and variances is difficult in many learning approaches,...
Hierarchical Models Of Variance Sources
Harri Valpola Markus, Markus Harva, Juha Karhunen
In many models, variances are assumed to be constant although this assumption is known to be unrealistic. Joint modelling of means and variances can lead to infinite probability densities which makes...