Markov Analysis and Planning

The past is often considered the best prophet of the future. As planning and forecasting future labor demand according to company internal promotion, reduction of employee's disparity, or withdrawal of employee disparity, review of Markov analysis chart can reflect organizational opportunities based on past employment change I can do it. ) Doortodoor Sports Equipment is unique in the industry and is the only company selling door to door. However, considering this strategy and other personnel practices, you can determine whether the changes used will improve retention or internal promotion.

This paper introduced comparative analysis of Hidden Markov Model (HMM), Maximum Entropy Markov Model (MEMM) and Conditional Random Field (CRF). HMM, MEMM and CRF are three common statistical modeling methods commonly used for pattern recognition and machine learning problems. Let's examine each method in more detail. HMM has a powerful statistical foundation and effective learning algorithms. With this algorithm learning can be done directly from the original sequence data. Insertion and deletion penalties can be handled consistently in the form of locally learnable methods and variable length input can be handled. They are the most flexible summary of array profiles. In addition, it can perform various operations such as multiple alignment, data mining and classification, structural analysis, pattern discovery and so on. It's easy to put it in a library

Andrei Markov studied the Markov chain in the early 20th century. Markov is interested in studying the extension of an independent random sequence whose motivation is incompatible with the opinion of Pavel Nekrasov. The first article by Marc in 1906 on Markov chain showed that there is no independent hypothesis as the average result of Markov chain converges to a fixed numerical vector under certain conditions. A number of weak laws are proved in this case. It is generally considered to be a requirement of this law of mathematics. Markov later used the Markov chain to study the distribution of vowels in Eugene Onezin by Alexander Pushkin and proved the central limit theorem of this chain.

Formally, the Markov chain is a probabilistic automaton. The probability distribution of state transition is usually expressed as a transition matrix of Markov chain. If the Markov chain has N possible states, the matrix is ​​an N × N matrix so that the entry (I, J) is the probability of transitioning from state I to state J. In addition, the transfer matrix must be a random matrix, that is, a matrix to which entries in each row are completely added. This is totally meaningful as each row represents its own probability distribution.