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Markov chain

Markov chain
A simple two-state Markov chain A Markov chain (discrete-time Markov chain or DTMC[1]), named after Andrey Markov, is a mathematical system that undergoes transitions from one state to another on a state space. It is a random process usually characterized as memoryless: the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of "memorylessness" is called the Markov property. Markov chains have many applications as statistical models of real-world processes. Introduction[edit] A Markov chain is a stochastic process with the Markov property. In literature, different Markov processes are designated as "Markov chains". The changes of state of the system are called transitions. A discrete-time random process involves a system which is in a certain state at each step, with the state changing randomly between steps. Many other examples of Markov chains exist. Formal definition[edit] . . of states as input, where is defined, while and

Related:  Section 10: Markov ProcessesArt & Technology

Application to Markov Chains Application to Markov Chains Introduction Suppose there is a physical or mathematical system that has n possible states and at any one time, the system is in one and only one of its n states. As well, assume that at a given observation period, say k th period, the probability of the system being in a particular state depends only on its status at the k-1st period. Such a system is called Markov Chain or Markov process. Blog » Blog Archive » Pimp up your camera with Arduino timelapse video tutorial – auch auf Deutsch Pimp up your camera with Arduino timelapse video tutorial – auch auf Deutsch Zoe Romano — May 25th, 2013 Last month we launched the first of a series of tutorials hosted on our Youtube Channel and created by Max of MaxTechTV in german language. Today we are publishing the second video called “Pimp-up your camera with an Arduino timelapse“.

Markov chains don’t converge I often hear people often say they’re using a burn-in period in MCMC to run a Markov chain until it converges. But Markov chains don’t converge, at least not the Markov chains that are useful in MCMC. These Markov chains wander around forever exploring the domain they’re sampling from. Any point that makes a “bad” starting point for MCMC is a point you might reach by burn-in. Not only that, Markov chains can’t remember how they got where they are.

Molecular diffusion Diffusion from a microscopic and macroscopic point of view. Initially, there are solute molecules on the left side of a barrier (purple line) and none on the right. The barrier is removed, and the solute diffuses to fill the whole container. 4 Problems with Big Data (And How to Solve Them) Big Data Innovation Summit returns to Boston, Sep 9-10, with 60+ sessions covering Big Data biggest problems (and how to solve them). Use code KD300 to save $300 off all two-day pass prices. Managing large datasets can be problematic. Compared to smaller amounts of data, analysis, storage, privacy and interpretation can cause difficulties for today's data leaders. Rest assured, the Big Data Innovation Summit returns to Boston next month, on September 9-10, with 60+ sessions covering Big Data's biggest problems (and how to solve them!)

Examples of Markov chains This page contains examples of Markov chains in action. Board games played with dice[edit] A game of snakes and ladders or any other game whose moves are determined entirely by dice is a Markov chain, indeed, an absorbing Markov chain. This is in contrast to card games such as blackjack, where the cards represent a 'memory' of the past moves. To see the difference, consider the probability for a certain event in the game. In the above mentioned dice games, the only thing that matters is the current state of the board. Predator takes visual object tracking to new heights – Computer Chips & Hardware Technology Cameras have slowly made their way into the portable gadgets we all carry around with us and not having a camera in a new device is viewed as a missing feature. It’s got to the point now where the latest smartphones even have two cameras so as to make for better video chat. But while the prevalence and quality of the cameras has gone up, the software still lags behind in terms of being able to identify and track objects in any real-time or captured footage.

Random walk Example of eight random walks in one dimension starting at 0. The plot shows the current position on the line (vertical axis) versus the time steps (horizontal axis). A random walk is a mathematical formalization of a path that consists of a succession of random steps.