A Data Science Central Community

In particular, how do you assess whether a particular spatial process, based on one observation of the process in question, belongs to a particular family of stochastic spatial processes.

For instance, let's imagine that you observe star distributions. You build a theoretical model of cluster processes to model star distributions: for instance, a Poisson process to model centroids of star clusters, and at each point (centroid) of the parent process, a second process consisting of points (stars) randomly distributed in a circle of radius R around the star cluster's centroid (R being itself a random variable).

Which metric would you use to assess whether your observation (star distribution) fits with the theoretical model? Which simple functional uniquely identifies a specific spatial process? I'm looking at very simple metrics such as star counts within some regions, and distances between stars (mean, standard deviation, distribution).

For instance, let's imagine that you observe star distributions. You build a theoretical model of cluster processes to model star distributions: for instance, a Poisson process to model centroids of star clusters, and at each point (centroid) of the parent process, a second process consisting of points (stars) randomly distributed in a circle of radius R around the star cluster's centroid (R being itself a random variable).

Which metric would you use to assess whether your observation (star distribution) fits with the theoretical model? Which simple functional uniquely identifies a specific spatial process? I'm looking at very simple metrics such as star counts within some regions, and distances between stars (mean, standard deviation, distribution).

Tags:

© 2021 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions