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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:  Art & Technology

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“. The video explains how to connect an Arduino UNO with you camera and shoot pictures, for example, every 1, 5, 10 seconds to create wonderful videos of slow processes that would normally appear subtle to the human eye. Enjoy the tutorial below and share with us the results of your experimentations! Letzten Monat haben wir das erste einer Reihe von Video-Tutorials auf unserem YouTube Kanal veröffentlicht. Heute veröffentlichen wir das zweite Video mit dem Titel “Erstelle tolle Zeitrafferaufnahmen mit deiner Kamera & Arduino”.

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. That’s their defining property. When someone says a Markov chain has converged, they may mean that the chain has entered a high-probability region. Burn-in may be ineffective. Why use burn-in? So why does it matter whether you start your Markov chain in a high-probability region? Samples from Markov chains don’t converge, but averages of functions applied to these samples may converge. It’s not just a matter of imprecise language when people say a Markov chain has converged.

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!) Use discount code KD300 and save $300 off all two-day pass prices. Check out the schedule here: bit.ly/dataeast15 If you are interested in attending or have any questions please contact Hayley Law at hlaw@theiegroup.com (+1 415 692 5378) quoting your discount code, or you can confirm your discounted pass here: bit.ly/1L1eysV 1) Where should I store it? The more data an organization has, the more complex the problems of managing it can become. 3) False Positives

Normative ethics Study of ethical action;branch of philosophical ethics that investigates the set of questions that arise when considering how one ought to act,morally speaking;examines standards for the rightness and wrongness of actions;question:"What should I do?" Normative ethics is the study of ethical action. It is the branch of philosophical ethics that investigates the set of questions that arise when considering how one ought to act, morally speaking. Normative ethics is distinct from meta-ethics because it examines standards for the rightness and wrongness of actions, while meta-ethics studies the meaning of moral language and the metaphysics of moral facts; and it is distinct from applied ethics in that the former is more concerned with 'who ought one be' rather than the ethics of a specific issue (such as if, or when, abortion is acceptable). Normative ethics is also distinct from descriptive ethics, as the latter is an empirical investigation of people’s moral beliefs. Binding force[edit]

Google search: 15 hidden features 3. Conversions Currency conversions and unit conversions can be found by using the syntax: <amount><unit1> in <unit2>. So for example, you could type '1 GBP in USD', '20 C in F' or '15 inches in cm' and get an instant answer. 4. Search for 'time in <place>' and you will get the local time for that place, as well as the time zone it is in. 5. A quick way to translate foreign words is to type 'translate <word> to <language>'. 6. If you know you are looking for a PDF or a Word file, you can search for specific file types by typing '<search term> filetype:pdf' or '<search term> filetype:doc' 7. If you type in a flight number, the top result is the details of the flight and its status. 8. Search for film showings in your area by typing 'films' or 'movies' followed by your postcode. 9. 10. When you're enter a search term that has a second meaning, or a close association with something else, it can be difficult to find the results you want. 11. 12. 13. 14. 15.

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. That is about to change due to the work of Czech student Zdeneki Kalai. This algorithm not only tracks, it learns the more it gets used. Kalai believes what he has created, a combination of tracking, learning, and detection is completely unique and allows for a whole new set of functionality to be applied when looking at video feeds. In the video he gives examples of a new interface that can detect your fingers and use them to draw, learning to get better at trakcing those fingers as it is used more. Read more at GottaBeMobile

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. For example, the path traced by a molecule as it travels in a liquid or a gas, the search path of a foraging animal, the price of a fluctuating stock and the financial status of a gambler can all be modeled as random walks, although they may not be truly random in reality. Various different types of random walks are of interest. . is defined for the continuum of times . Lattice random walk[edit] A popular random walk model is that of a random walk on a regular lattice, where at each step the location jumps to another site according to some probability distribution. One-dimensional random walk[edit] An elementary example of a random walk is the random walk on the integer number line, This walk can be illustrated as follows. .

Anna Karenina principle The Anna Karenina principle describes an endeavor in which a deficiency in any one of a number of factors dooms it to failure. Consequently, a successful endeavor (subject to this principle) is one where every possible deficiency has been avoided. The name of the principle derives from Leo Tolstoy's book Anna Karenina, which begins: Happy families are all alike; every unhappy family is unhappy in its own way. In statistics, the term Anna Karenina principle is used to describe significance tests: there are any number of ways in which a dataset may violate the null hypothesis and only one in which all the assumptions are satisfied. Examples[edit] Failed domestication[edit] The Anna Karenina principle was popularized by Jared Diamond in his book Guns, Germs and Steel.[1] Diamond uses this principle to illustrate why so few wild animals have been successfully domesticated throughout history, as a deficiency in any one of a great number of factors can render a species undomesticable.

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