8,023 research outputs found
A Tutorial on Fisher Information
In many statistical applications that concern mathematical psychologists, the
concept of Fisher information plays an important role. In this tutorial we
clarify the concept of Fisher information as it manifests itself across three
different statistical paradigms. First, in the frequentist paradigm, Fisher
information is used to construct hypothesis tests and confidence intervals
using maximum likelihood estimators; second, in the Bayesian paradigm, Fisher
information is used to define a default prior; lastly, in the minimum
description length paradigm, Fisher information is used to measure model
complexity
Psychophysical identity and free energy
An approach to implementing variational Bayesian inference in biological
systems is considered, under which the thermodynamic free energy of a system
directly encodes its variational free energy. In the case of the brain, this
assumption places constraints on the neuronal encoding of generative and
recognition densities, in particular requiring a stochastic population code.
The resulting relationship between thermodynamic and variational free energies
is prefigured in mind-brain identity theses in philosophy and in the Gestalt
hypothesis of psychophysical isomorphism.Comment: 22 pages; published as a research article on 8/5/2020 in Journal of
the Royal Society Interfac
The Price equation program: simple invariances unify population dynamics, thermodynamics, probability, information and inference
The fundamental equations of various disciplines often seem to share the same
basic structure. Natural selection increases information in the same way that
Bayesian updating increases information. Thermodynamics and the forms of common
probability distributions express maximum increase in entropy, which appears
mathematically as loss of information. Physical mechanics follows paths of
change that maximize Fisher information. The information expressions typically
have analogous interpretations as the Newtonian balance between force and
acceleration, representing a partition between direct causes of change and
opposing changes in the frame of reference. This web of vague analogies hints
at a deeper common mathematical structure. I suggest that the Price equation
expresses that underlying universal structure. The abstract Price equation
describes dynamics as the change between two sets. One component of dynamics
expresses the change in the frequency of things, holding constant the values
associated with things. The other component of dynamics expresses the change in
the values of things, holding constant the frequency of things. The separation
of frequency from value generalizes Shannon's separation of the frequency of
symbols from the meaning of symbols in information theory. The Price equation's
generalized separation of frequency and value reveals a few simple invariances
that define universal geometric aspects of change. For example, the
conservation of total frequency, although a trivial invariance by itself,
creates a powerful constraint on the geometry of change. That constraint plus a
few others seem to explain the common structural forms of the equations in
different disciplines. From that abstract perspective, interpretations such as
selection, information, entropy, force, acceleration, and physical work arise
from the same underlying geometry expressed by the Price equation.Comment: Version 3: added figure illustrating geometry; added table of symbols
and two tables summarizing mathematical relations; this version accepted for
publication in Entrop
Equations defining probability tree models
Coloured probability tree models are statistical models coding conditional
independence between events depicted in a tree graph. They are more general
than the very important class of context-specific Bayesian networks. In this
paper, we study the algebraic properties of their ideal of model invariants.
The generators of this ideal can be easily read from the tree graph and have a
straightforward interpretation in terms of the underlying model: they are
differences of odds ratios coming from conditional probabilities. One of the
key findings in this analysis is that the tree is a convenient tool for
understanding the exact algebraic way in which the sum-to-1 conditions on the
parameter space translate into the sum-to-one conditions on the joint
probabilities of the statistical model. This enables us to identify necessary
and sufficient graphical conditions for a staged tree model to be a toric
variety intersected with a probability simplex.Comment: 22 pages, 4 figure
Bayesian non-linear large scale structure inference of the Sloan Digital Sky Survey data release 7
In this work we present the first non-linear, non-Gaussian full Bayesian
large scale structure analysis of the cosmic density field conducted so far.
The density inference is based on the Sloan Digital Sky Survey data release 7,
which covers the northern galactic cap. We employ a novel Bayesian sampling
algorithm, which enables us to explore the extremely high dimensional
non-Gaussian, non-linear log-normal Poissonian posterior of the three
dimensional density field conditional on the data. These techniques are
efficiently implemented in the HADES computer algorithm and permit the precise
recovery of poorly sampled objects and non-linear density fields. The
non-linear density inference is performed on a 750 Mpc cube with roughly 3 Mpc
grid-resolution, while accounting for systematic effects, introduced by survey
geometry and selection function of the SDSS, and the correct treatment of a
Poissonian shot noise contribution. Our high resolution results represent
remarkably well the cosmic web structure of the cosmic density field.
Filaments, voids and clusters are clearly visible. Further, we also conduct a
dynamical web classification, and estimated the web type posterior distribution
conditional on the SDSS data.Comment: 18 pages, 11 figure
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