Statistical-based language models have a natural advantage over rule-based language models, while (Chinese) word segmentation is the basis of natural language processing, next, we will introduce statistics-based Chinese Word Segmentation and part-of-speech tagging. To this end, make the following arrangements: first introduce the basic concepts involved in Chinese processing, and then analyze some open-source Chinese Word Segmentation principles based on statistics.
The basic concepts involved i

1. Description of the problem
Markov chain algorithm is used to generate a random English, its idea is very simple. Read the data first, then divide the read data into prefixes and suffixes, and obtain the suffix randomly by prefix, which produces a readable random English.
To illustrate the convenience, suppose we have the following passage:
Copy Code code as follows:
Show your flowcharts a

You can create a recursive neural network with one character and one word Fu to learn the language features. But what is the difference between this result model and the Markov chain designed for the same purpose? This paper uses R to implement a character-character Markov chain to explore.Andrej Karpathy's article "Th

This article describes how to generate pseudo-random text based on the Markov chain in Python. it is a small implementation based on the Markov algorithm and fully reflects the use of Python in scientific computing, for more information, see the definition of Wolfram.
A Markov cha

A tutorial on generating pseudo-random text based on the Markov chain in Python,
First, let's take a look at the definition from Wolfram.
A Markov chain is a set of random variables {X_t} (t runs through 0, 1,...). Given the current state, the future is independent of the past conditions.
Definition of Wikipedia is cle

First look at the definition from Wolfram
Markov chain is a set of random variable {x_t} (t runs through the 0,1,... ), given the current state, the future is independent of the past conditions.
The definition of Wikipedia is a bit clearer.
... Markov chains are stochastic processes with Markov properties. [This mea

Markov process:
The Markov process is divided into three kinds according to whether the state and time parameters are continuous or discrete: 1. The time and the state are all discrete called Markov chains, 2. Time and state are called Markov processes continuously, 3. Time continuous, state discrete

The Markov chain is defined as followsIt is easy to see from the definition that the current state of the Markov chain is only relevant to the previous state. For example, we forecast the weather tomorrow, only consider today's weather conditions, regardless of yesterday's weather conditions.Give a concrete example. So

First, what is the Markov chain Monte Carlo (MCMC) method?
The shortest answer is:
"MCMC is a method to approximate the posterior distribution of interested parameters by random sampling in probabilistic space."
In this article, I can explain this short answer without any mathematical knowledge.
Basic terminology of Bayesian theory system
First, there are some terms.
The parameters of interest are ju

Markov chain is a random processThe probability of moving from part m of the state I of part N to state J is irrelevant to the probability before N, which we call the Markov chain.If the N-part probability is independent of time, then it becomes the Markov chain of the homog

Use python to analyze the Markov Chain Algorithm Instances and use python to analyze instances.
This article describes how to implement the Markov Chain Algorithm in python. Share it with you for your reference. The specific analysis is as follows:
In The program design Practice (The Practice of Programming), Chapter

Test instructions: Given a n*n map, above the K barrier point can not go, there is a robot from (0,0), each time such as the probability of the fixed or to the upper and lower left without obstacles to move around, asked to walk infinite steps after the probability of stopping in the right lower part of the graphNIdea: It is said to find the lawProof from 78494788Random walk model of Markov chain
The st

This article mainly introduces how to implement the Markov chain algorithm in python. The example analyzes the principles and implementation skills of the Markov chain algorithm, for more information, see the example in this article. Share it with you for your reference. The specific analysis is as follows:
In The p

In this paper, the method of realizing the algorithm of the Markov chain by Python is described. Share to everyone for your reference. The specific analysis is as follows:
In the "Programming Practice" (English name "The Practice of Programming") of the book, the third chapter of the C language, C++,awk and Perl respectively, the implementation of the MA-fu chain

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