(testing signal)

Tag: sampling

Cluster Sampling

Cluster sampling is a method of obtaining a representative sample from a population that researchers have divided into groups. An individual cluster is a subgroup that mirrors the diversity of the whole population while the set of clusters are similar to each other. Typically, researchers use this approach when studying large, geographically dispersed populations because it is a cost-controlling measure. Researchers do not need to obtain samples from all clusters because each one reflects…… Read more...

Stratified Sampling

Stratified sampling is a method of obtaining a representative sample from a population that researchers have divided into relatively similar subpopulations (strata). Researchers use stratified sampling to ensure specific subgroups are present in their sample. It also helps them obtain precise estimates of each group’s characteristics. Many surveys use this method to understand differences between subpopulations better. Stratified sampling is also known as stratified random sampling.
The……

How to Determine the Best Fitting Data Distribution Using Python – KDnuggets

Sometimes you know the best fitting distribution, or probability density function, of your data prior to analysis; more often, you do not. Approaches to data sampling, modeling, and analysis can vary based on the distribution of your data, and so determining the best fit theoretical distribution can be an essential step in your data exploration process.

This is where distfit comes in.

distfit is a python package for probability density fitting across 89 univariate distributions to non-censored data by residual sum of squares (RSS), and hypothesis testing. Probability density fitting is the fitting of a probability distribution to a series of data concerning the repeated measurement of a variable phenomenon.… Read more...

Basics of Markov Chain Monte Carlo Algorithms | by Sheharyar Akhtar | Sep, 2021

The aim of this article is to give a conceptual understanding of Markov Chains and why we use them.

Markov Chain Monte Carlo is a group of algorithms used to map out the posterior distribution by sampling from the posterior distribution. The reason we use this method instead of the quadratic approximation method is because when we encounter distributions that have multiple peaks, it is possible that the algorithm will converge to a local maxima, and not give us the true approximation of the posterior distribution. The Monte Carlo algorithms however, use the principles of randomness and chaos theory to solve problems that would otherwise be difficult, if not impossible, to solve analytically.


Multi-Armed Bandits: Thompson Sampling Algorithm

You and your friend have been using bandit algorithms to optimise which restaurants and movies to choose for your weekly movie night. So far, you have tried different bandits algorithms like Epsilon-Greedy, Optimistic Initial Values and Upper Confidence Bounds (UCB). You’ve found the UCB1-Tuned algorithm to work slightly better than the rest, for both Bernoulli and Normal rewards, and have ended up using it for the last few months.

Even though your movie nights have been going great with the choices made by UCB1-Tuned, you miss the thrill of trying a new algorithm out.

“Have you heard of Thompson Sampling?”


Methane in plumes of Saturn's moon Enceladus: Possible signs of life?

An unknown methane-producing process is likely at work in the hidden ocean beneath the icy shell of Saturn’s moon Enceladus, suggests a new study published in Nature Astronomy by scientists at the University of Arizona and Paris Sciences & Lettres University.

Giant water plumes erupting from Enceladus have long fascinated scientists and the public alike, inspiring research and speculation about the vast ocean that is believed to be sandwiched between the moon’s rocky core and its icy shell. Flying through the plumes and sampling their chemical makeup, the Cassini spacecraft detected a relatively high concentration of certain molecules associated with hydrothermal vents on the bottom of Earth’s oceans, specifically dihydrogen, methane and carbon dioxide.


Blockchain Main Challenges and Issues

Almost 5 years have passed since  I got into BC technologý and its still the same issue:  its overvalued in their expectatives. Lots of money in ideas and projects that cant be implemented. SC cant work outside the BC and of course cant interact with the physical world.

Blockchains need realiable interfaces  and thats a major weakness-

Cada vez aque un programa o contrato o usuario hace algo, todos los nodos de la red deben acordar que la acción ha tenido lugar y para ello deben hacer funcionar el mismo contrato. He aquí una restricción de escala, ya que obviamente esto no es sostenible: no puedes tener miles de millones de nodos ejecutando los mismos contratos para validarlos.… Read more...