Sodium-potassium-adenosinetriphosphatase-dependent sodium transport in the kidney: hormonal control
E Feraille, A Doucet - Physiological reviews, 2001 - journals.physiology.org
Tubular reabsorption of filtered sodium is quantitatively the main contribution of kidneys to
salt and water homeostasis. The transcellular reabsorption of sodium proceeds by a two-step …
salt and water homeostasis. The transcellular reabsorption of sodium proceeds by a two-step …
[PDF][PDF] A tutorial on particle filtering and smoothing: Fifteen years later
A Doucet, AM Johansen - Handbook of nonlinear filtering, 2009 - warwick.ac.uk
Optimal estimation problems for non-linear non-Gaussian state-space models do not typically
admit analytic solutions. Since their introduction in 1993, particle filtering methods have …
admit analytic solutions. Since their introduction in 1993, particle filtering methods have …
On particle methods for parameter estimation in state-space models
Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …
information engineering and signal processing. Particle methods, also known as Sequential …
[BOOK][B] Sequential Monte Carlo methods in practice
Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as
financial modelling, target tracking and computer vision. These methods, appearing under …
financial modelling, target tracking and computer vision. These methods, appearing under …
On sequential Monte Carlo sampling methods for Bayesian filtering
In this article, we present an overview of methods for sequential simulation from posterior
distributions. These methods are of particular interest in Bayesian filtering for discrete time …
distributions. These methods are of particular interest in Bayesian filtering for discrete time …
[HTML][HTML] An introduction to MCMC for machine learning
This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method
with emphasis on probabilistic machine learning. Second, it reviews the main building …
with emphasis on probabilistic machine learning. Second, it reviews the main building …
Sequential monte carlo samplers
We propose a methodology to sample sequentially from a sequence of probability distributions
that are defined on a common space, each distribution being known up to a normalizing …
that are defined on a common space, each distribution being known up to a normalizing …
An introduction to sequential Monte Carlo methods
Many real-world data analysis tasks involve estimating unknown quantities from some given
observations. In most of these applications, prior knowledge about the phenomenon being …
observations. In most of these applications, prior knowledge about the phenomenon being …
The unscented particle filter
R Van Der Merwe, A Doucet… - Advances in neural …, 2000 - proceedings.neurips.cc
In this paper, we propose a new particle filter based on sequential importance sampling.
The algorithm uses a bank of unscented fil (cid: 173) ters to obtain the importance proposal …
The algorithm uses a bank of unscented fil (cid: 173) ters to obtain the importance proposal …
Particle markov chain monte carlo methods
Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two
main tools to sample from high dimensional probability distributions. Although asymptotic …
main tools to sample from high dimensional probability distributions. Although asymptotic …