Computational Role of Astrocytes in Bayesian Inference and Probability Distribution Encoding

2016 ◽  
pp. 24-33
Author(s):  
Martin Dimkovski ◽  
Aijun An
Keyword(s):  
Bayesian Inference ◽  
Probability Distribution

scholarly journals A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guo-Zheng Wang ◽  
Li Xiong ◽  
Hu-Chen Liu
Keyword(s):  
Monte Carlo ◽  
Bayesian Inference ◽  
Probability Distribution ◽  
Community Detection ◽  
Degree Distribution ◽  
Prior Probability ◽  
Bipartite Networks ◽  
Inference Method ◽  
Prior Probability Distribution ◽  
Bayesian Inference Method

Community detection is an important analysis task for complex networks, including bipartite networks, which consist of nodes of two types and edges connecting only nodes of different types. Many community detection methods take the number of communities in the networks as a fixed known quantity; however, it is impossible to give such information in advance in real-world networks. In our paper, we propose a projection-free Bayesian inference method to determine the number of pure-type communities in bipartite networks. This paper makes the following contributions: (1) we present the first principle derivation of a practical method, using the degree-corrected bipartite stochastic block model that is able to deal with networks with broad degree distributions, for estimating the number of pure-type communities of bipartite networks; (2) a prior probability distribution is proposed over the partition of a bipartite network; (3) we design a Monte Carlo algorithm incorporated with our proposed method and prior probability distribution. We give a demonstration of our algorithm on synthetic bipartite networks including an easy case with a homogeneous degree distribution and a difficult case with a heterogeneous degree distribution. The results show that the algorithm gives the correct number of communities of synthetic networks in most cases and outperforms the projection method especially in the networks with heterogeneous degree distributions.

Bayesian Inference of Information Theoretic Metrics of Anonymity

2014 ◽  
Vol 989-994 ◽  
pp. 4680-4683
Author(s):  
Han Ru Pei ◽  
Zhi Jian Wang ◽  
Yu Wang
Keyword(s):  
Bayesian Inference ◽  
Probability Distribution ◽  
Inference Method ◽  
Information Theoretic ◽  
System Parameters ◽  
Bayesian Inference Method

Information theoretic metrics is popular theory to measure anonymity. However the difficulty in getting the probability distribution of subjects hampers its practical usage. In this paper we propose a Bayesian inference method to tackle this problem. Our method makes it possible to compare the anonymity of different anonymous systems. We use this method to analyze Threshold Mix and point out different system parameters which do and do not have influence on anonymity.

Does firm performance reveal its own causes? the role of Bayesian inference

2010 ◽  
Vol 31 (1) ◽  
pp. 39-57 ◽  
Author(s):  
Ying-Chan Tang ◽  
Fen-May Liou
Keyword(s):  
Bayesian Inference ◽  
Firm Performance

Bayesian Inference for the Demand of Engineering Products

2012 ◽  
Author(s):  
Ali E. Abbas ◽  
George A. Hazelrigg ◽  
Mahmood Alkindi
Keyword(s):  
Bayesian Inference ◽  
Probability Distribution ◽  
Expected Utility ◽  
Prior Probability ◽  
Consumer Preferences ◽  
Demand Estimation ◽  
Market Analysis ◽  
Design Parameters ◽  
The Given ◽  
Multiple Units

Within the context of a profit making firm, the job of a design engineer is to choose design parameters and product attributes that maximize the expected utility of profit. To do this effectively, the engineer needs to have an estimate of the demand for the product as a function of its price and its attributes. The firm may conduct a survey to elicit consumer preferences for the product at a given price and would like to update their belief about demand given the survey data. The purpose of this paper is to present a Bayesian methodology for demand estimation that meets this need. The estimation process begins with a prior probability distribution of demand at a given price. Using Bayesian analysis, we show how to update demand for the product given various pieces of information such as market analysis, polls and a variety of other methods. We also discuss situations where consumers can demand multiple units of the product at the given price.

scholarly journals Cognitive Bare Bones Particle Swarm Optimisation with Jumps

2016 ◽  
Vol 7 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Mohammad Majid al-Rifaie ◽  
Tim Blackwell
Keyword(s):  
Probability Distribution ◽  
Particle Swarm ◽  
Search Space ◽  
Particle Swarm Optimisation ◽  
Early Stages ◽  
Bare Bones

The ‘bare bones' (BB) formulation of particle swarm optimisation (PSO) was originally advanced as a model of PSO dynamics. The idea was to model the forces between particles with sampling from a probability distribution in the hope of understanding swarm behaviour with a conceptually simpler particle update rule. ‘Bare bones with jumps' (BBJ) proposes three significant extensions to the BB algorithm: (i) two social neighbourhoods, (ii) a tuneable parameter that can advantageously bring the swarm to the ‘edge of collapse' and (iii) a component-by-component probabilistic jump to anywhere in the search space. The purpose of this paper is to investigate the role of jumping within a specific BBJ algorithm, cognitive BBJ (cBBJ). After confirming the effectiveness of cBBJ, this paper finds that: jumping in one component only is optimal over the 30 dimensional benchmarks of this study; that a small per particle jump probability of 1/30 works well for these benchmarks; jumps are chiefly beneficial during the early stages of optimisation and finally this work supplies evidence that jumping provides escape from regions surrounding sub-optimal minima.

The effect of weak Brownian rotations on particles in shear flow

1971 ◽  
Vol 46 (4) ◽  
pp. 685-703 ◽  
Author(s):  
L. G. Leal ◽  
E. J. Hinch
Keyword(s):  
Reynolds Number ◽  
Steady State ◽  
Probability Distribution ◽  
Shear Flow ◽  
Equilibrium Distribution ◽  
Steady State Probability ◽  
Bulk Properties ◽  
Closed Orbits ◽  
Basic Equations

Axisymmetric particles in zero Reynolds number shear flow execute closed orbits. In this paper we consider the role of small Brownian couples in establishing a steady-state probability distribution for a particle being on any particular orbit. After presenting the basic equations, we derive an expression for the equilibrium distribution. This result is then used to calculate some bulk properties for a suspension of such particles, and these predicted properties are compared with available experimental observation.

Introduction

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Probability Distribution ◽  
Generation Process ◽  
Stationary Probability Distribution ◽  
Important Distinction ◽  
Scope Of Application ◽  
Stochastic Graph ◽  
Mathematical Connection ◽  
Graph Generation

This introductory chapter sets the scene for the material which follows by briefly introducing the study of networks and describing their wide scope of application. It discusses the role of well-specified random graphs in setting network science onto a firm scientific footing, emphasizing the importance of well-defined null models. Non-trivial aspects of graph generation are introduced. An important distinction is made between approaches that begin with a desired probability distribution on the final graph ensembles and approaches where the graph generation process is the main object of interest and the challenge is to analyze the expected topological properties of the generated networks. At the core of the graph generation process is the need to establish a mathematical connection between the stochastic graph generation process and the stationary probability distribution to which these processes evolve.

scholarly journals ENTROPY ANALYSIS OF WORD-LENGTH SERIES OF NATURAL LANGUAGE TEXTS: EFFECTS OF TEXT LANGUAGE AND GENRE

2012 ◽  
Vol 22 (09) ◽  
pp. 1250223 ◽  
Author(s):  
MARIA KALIMERI ◽  
VASSILIOS CONSTANTOUDIS ◽  
CONSTANTINOS PAPADIMITRIOU ◽  
KONSTANTINOS KARAMANOS ◽  
FOTIS K. DIAKONOS ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Natural Language ◽  
Crucial Role ◽  
Word Length ◽  
Entropy Analysis ◽  
N Gram ◽  
Text Language ◽  
The Impact

We estimate the n-gram entropies of natural language texts in word-length representation and find that these are sensitive to text language and genre. We attribute this sensitivity to changes in the probability distribution of the lengths of single words and emphasize the crucial role of the uniformity of probabilities of having words with length between five and ten. Furthermore, comparison with the entropies of shuffled data reveals the impact of word length correlations on the estimated n-gram entropies.

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