# The Liang-Kleeman Information Flow: Theory and Applications

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## Abstract

**:**

## 1. Introduction

## 2. Mathematical Formalism

#### 2.1. Theoretical Framework

#### 2.2. Toward a Rigorous Formalism—A Heuristic Argument

#### 2.3. Mathematical Formalism

## 3. Discrete Systems

#### 3.1. Frobenius-Perron Operator

**Figure 1.**Illustration of the Frobenius-Perron operator $\mathcal{P}$, which takes $\rho \left(\mathit{x}\right)$ to $\mathcal{P}\rho \left(\mathit{x}\right)$ as Φ takes $\mathit{x}$ to $\Phi \mathit{x}$.

#### 3.2. Information Flow

#### 3.3. Properties

**Theorem 3.1**

**Theorem 3.2**

## 4. Continuous Systems

- Discretize the continuous system in time on $[t,t+\Delta t]$, and construct a mapping Φ to take $\mathit{x}\left(t\right)$ to $\mathit{x}(t+\Delta t)$;
- Freeze ${x}_{2}$ in Φ throughout $[t,t+\Delta t]$ to obtain a modified mapping ${\Phi}_{\setminus \phantom{\rule{-3.00003pt}{0ex}}2}$;
- Compute the marginal entropy change $\Delta {H}_{1}$ as Φ steers the system from t to $t+\Delta t$;
- Derive the marginal entropy change $\Delta {H}_{1\setminus \phantom{\rule{-3.00003pt}{0ex}}2}$ as ${\Phi}_{\setminus \phantom{\rule{-3.00003pt}{0ex}}2}$ steers the modified system from t to $t+\Delta t$;
- Take the limit$${T}_{2\to 1}=\underset{\Delta t\to 0}{lim}\frac{\Delta {H}_{1}-\Delta {H}_{1\setminus \phantom{\rule{-3.00003pt}{0ex}}2}}{\Delta t}$$

#### 4.1. Discretization of the Continuous System

#### 4.2. Information Flow

**Proposition 4.1**

**Theorem 4.1**

#### 4.3. Properties

**Theorem 4.2**

**Theorem 4.3 (Causality)**

## 5. Stochastic Systems

**Proposition 5.1**

**Theorem 5.1**

**Theorem 5.2**

**Theorem 5.3**

## 6. Applications

#### 6.1. Baker Transformation

#### 6.2. H$\stackrel{\xb4}{\mathrm{e}}$non Map

**Figure 3.**A trajectory of the canonical H$\stackrel{\xb4}{\mathrm{e}}$non map ($a=1.4$, $b=0.3$) starting at $({x}_{1},{x}_{2})=(1,0)$.

#### 6.3. Truncated Burgers–Hopf System

- Initialize the joint density of $({x}_{1},{x}_{2},{x}_{3},{x}_{4})$ with some distribution ${\rho}_{0}$; make random draws according to ${\rho}_{0}$ to form an ensemble. The ensemble should be large enough to resolve adequately the sample space.
- Discretize the sample space into “bins.”
- Do ensemble prediction for the system (73)–(74).
- At each step, estimate the probability density function ρ by counting the bins.
- Plug the estimated ρ back to Equation (39) to compute the rates of information flow at that step.

**Figure 4.**The invariant attractor of the truncated Burgers–Hopf system (73)–(76). Shown here is the trajectory segment for $2\le t\le 20$ starting at $(40,40,40,40)$. (a) and (b) are the 3-dimensional projections onto the subspaces ${x}_{1}$-${x}_{2}$-${x}_{3}$ and ${x}_{2}$-${x}_{3}$-${x}_{4}$, respectively.

**Figure 5.**Information flows within the 4D truncated Burgers-Hopf system. The series prior to $t=2$ are not shown because some trajectories have not entered the attractor by that time.

#### 6.4. Langevin Equation

**Figure 6.**A solution of Equation (78), the model examined in [54], with ${a}_{21}=0$ and initial conditions as shown in the text: (

**a**) $\mathbf{\mu}$; (

**b**) Σ; and (

**c**) a sample path starting from (1,2).

**Figure 7.**The computed rates of information flow for the system (77): (a) ${T}_{2\to 1}$, (b) ${T}_{1\to 2}$.

## 7. Summary

## Acknowledgments

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Liang, X.S.
The Liang-Kleeman Information Flow: Theory and Applications. *Entropy* **2013**, *15*, 327-360.
https://doi.org/10.3390/e15010327

**AMA Style**

Liang XS.
The Liang-Kleeman Information Flow: Theory and Applications. *Entropy*. 2013; 15(1):327-360.
https://doi.org/10.3390/e15010327

**Chicago/Turabian Style**

Liang, X. San.
2013. "The Liang-Kleeman Information Flow: Theory and Applications" *Entropy* 15, no. 1: 327-360.
https://doi.org/10.3390/e15010327