Bamimore Ayorinde’s Post

Simple method for calculating decision criteria weights: Improving human decision intelligence with Julia code https://lnkd.in/gHtdz8Ga

Machine Vitals plays a crucial role in postmortem analysis, providing deep insights into the health and performance of industrial equipment. Its advanced analytics help to pinpoint the root cause of failures, offering essential data to understand the "what" and "why" behind breakdowns. Learn more about machine vitals at - https://lnkd.in/gY8Ri8T4.

📢 Excited to share our latest blog post on "Numerical Analysis of Locally Adaptive Penalty Methods For The Navier-Stokes Equations"! This insightful report explores the development of an adaptive penalty scheme for the non-linear, time-dependent incompressible Navier-Stokes equations. It focuses on proving unconditional stability, control of $\

When you get a poor result in your measurement system analysis, you can sometimes get a deeper insight into the solution by using a designed experiment to understand your measurement error.... Roll the video... https://lnkd.in/eAjVHBjH

In the previous posts, I explained the covariate shift but missed the most important part… How can we detect it? This simple diagram is breaking down various covariate shift detection methods: 1) Multivariate - detects a shift in a joint(multiple features) distribution 2) Univariate - detects changes in a single feature distribution, using either: ↳ Statistical tests ↳ Distance methods Each of these methods has trade-offs based on the type of shift, but I will talk more about it in the next post👀

Graphs can provide LLMs with a more explicit representation of relationships and entities, enabling them to reason more effectively and avoid hallucinations. Graphs can be updated and expanded, allowing LLMs to incorporate new information as it becomes available.

. Which of the following is NOT a necessary condition for stationary time series? Answer : d a)      Mean is constant and does not depend on time b)     Autocovariance function depends on s and t only through their difference |s-t| (where t and s are moments in time) c)      The time series under considerations is a finite variance process d)     Time series is Gaussian 9. How many AR and MA terms should be included for the time series by looking at the above ACF and PACF plots? Answers: B Strong negative correlation at lag 1 suggest MA and there is only 1 significant lag. MA model is considered in the following situation, If the autocorrelation function (ACF) of the differenced series displays a sharp cutoff and/or the lag-1 autocorrelation is negative–i.e., if the series appears slightly “overdifferenced”–then consider adding an MA term to the model. The lag beyond which the ACF cuts off is the indicated number of MA terms. a.      AR(1) MA(0) b.      AR(0) MA(1) c.      AR(2) MA(1) d.      AR(1) MA(2) 10. Second differencing in time series can help to eliminate which trend? Answers : A a.      Seasonal Trend b.      linear Trend c.      Both A & B

Statistical inference in information geometry: Convert data into empirical distribution, and project it onto model submanifold wrt divergence: - Kullback-Leibler divergence projection: Maximum Likelihood Estimator - reverse KLD: Jaynes' Maximum Entropy principle 👉PDF: https://lnkd.in/gAbaBmg5

day 55/90: Duration: 3 hrs Today, 1) I learned about sampling bias. 2) Learned how to avoid sampling bias using split techniques that maintains the distribution of categorical features. 3) verified the categorical distribution in the original dataset and the sampled set. Good Night, peeps!

Overview of Classical Time Series Analysis: Techniques, Applications, and Models https://lnkd.in/dxSdnjPE