Cosmological data analysis using the MCMC technique—LambdaCDM model fitting using Python code.

✍️ About : Dive deep into the realm of cosmology with this comprehensive guide to analyzing cosmological data using the Markov Chain Monte Carlo (MCMC) technique. In this video tutorial, we explore the LambdaCDM model fitting process step-by-step, employing Python code for practical implementation. From understanding the theoretical background to hands-on application, this tutorial equips viewers with the knowledge and tools needed to navigate the complexities of cosmological data analysis.

Git-Hub repo link -- github.com/sleonardokap/cosmological_data
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📚 Research Articles :
My Research Papers and Other Important Research Articles:

[1] Dynamical stability of K-essence field interacting non-minimally with a perfect fluid -- arxiv.org/abs/2105.00361

[2] Dynamics of dark energy -- arxiv.org/abs/hep-th/0603057

[3] Dynamics of purely kinetic k-essence in presence of a perfect fluid -- arxiv.org/abs/2203.10607

[5] Dynamical Systems and Cosmology -- bit.ly/42KHcta

[6] Dynamical Systems in Cosmology -- bit.ly/3TOM9wV

[7] TASI Lectures on Inflation -- arxiv.org/abs/0907.5424

[8] Dynamical systems analysis of tachyon-dark-energy models from a new perspective -- doi.org/10.1103/PhysRevD.107.063515
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👽 Tags :
#cosmo_data #cosmological_data #data_analysis #mcmc #mcmc_cosmology #sstatistical_cosmology #LambdaCDM #inflation #SlowRollinflaion #SlowRollParameter #ultraslowrollinflation #InflationaryCosmology #dark_energy #dark_matter #Physics_research #Quintessence #kessence #dynamical_stability #CosmicInflation #GravitationalWaves #CosmicMicrowaveBackground #TachyonDynamics
#vscode
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🤯 Keywords:
Cosmological data analysis
MCMC technique
LambdaCDM model fitting
Python code
Cosmology
Markov Chain Monte Carlo
Data science
Astrophysics
VS code
Bayesian statistics
Scientific computing

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🎞️ Time interval:
0:00 -- Start
01:15 -- Physics of Model
13:30 -- Theoretical understanding of data analysis
27:23 -- Python Coding
49:00 -- Running your code on VS Code (Jupyter Notebook)
1:06:00 -- Windows Linux Subsy