Markov Chains in R Explained: Transition Matrices & the `markovchain` Class скачать в хорошем качестве

Markov Chains in R Explained: Transition Matrices & the `markovchain` Class

*Transition Matrices & the `markovchain` Class in R | Markov Chains Tutorial* In this video, Aaron Smith walks through the foundations of transition matrices and the `markovchain` package in R. If you're learning stochastic processes, preparing for a graduate‑level probability or statistics course, or working with Markov models in data science, this lesson gives you a clear, hands‑on introduction. You’ll see how to build transition matrices from scratch, generate random stochastic matrices, assign dimension names, and construct S4 `markovchain` objects. The video also demonstrates essential operations such as matrix powers, equality checks, absorbing states, steady states, transient states, and one‑step transition probabilities. --- *Topics Covered* What makes a valid transition matrix Identity, uniform, sticky uniform, and permutation matrices Generating random stochastic matrices (`spgs`, `RMAT`) Assigning row and column names in R Creating S4 `markovchain` objects with `new()` Multiplying Markov chains and probability vectors Computing matrix powers (e.g., \(P^{20}\)) Absorbing states and canonical form Conditional distributions and steady states Extracting one‑step transition probabilities Generic methods: `dim()`, `names()`, `summary()`, `plot()`, `predict()` --- *R Packages Used* `markovchain`, `spgs`, `RMAT`, `gtools`, `magrittr` --- *Who This Video Is For* Students in probability, stochastic processes, or spatial statistics Data scientists modeling sequential or state‑based systems R users exploring S4 classes and probabilistic modeling Anyone wanting a clean, reproducible workflow for Markov chains in R --- *Source Code* All code shown in the video is included in the RMarkdown file used to generate the lecture. --- If you find this helpful, consider liking the video, subscribing, and sharing it with classmates or colleagues who are learning Markov chains. More tutorials on spatial statistics, R programming, and reproducible workflows are on the way.