What is a quantitative organization? Quantitative classification of quantification

Quantitative organization is based on quantitative management ideas and methods, using systematic, scientific and quantitative means to quantitatively describe, evaluate and analyze various information and management objects in the organization, and make decisions and plans for the enterprise based on these results, so as to make the development of the enterprise in an orderly, transparent and effective control of the organizational form.

It covers all areas of enterprise management, such as the formulation of enterprise strategy, the construction of organizational system, and the quantitative management of specific work, and is a systematic quantitative management theory that solves enterprise problems as a whole.

The essence of quantitative organization is based on numbers, using mathematical methods to investigate and reflect the movement state and current situation of the enterprise, and make accurate numerical descriptions of the key decision-making points in the operation of the enterprise, the progress of the task, the scale of enterprise development, and the degree of goal realization, so that the enterprise can change from flat to three-dimensional, so that managers can more easily see the laws and trends behind the enterprise, so as to provide support for the follow-up adoption of various management means and countermeasures.

The various parts of the enterprise organization are interrelated, mutually restrictive, and combined into an organic whole, and the quantitative organization can use mathematical methods to reflect the "health status" of the organic whole and make adjustments, so that the enterprise management system can operate effectively like a sophisticated machine.

There are many, many types of oxidized tissues that can be queried on the web.

What is the amount of distribution organization, what is the amount of distribution organization, this has to go to the corresponding official network search to know.

Quantitative institutions are institutions engaged in quantitative investment. Quantitative investment refers to the trading method of issuing trading orders through quantitative methods and computer programming for the purpose of obtaining stable returns. In a broad sense, it can be regarded as a quantitative investment, and any investment method realized with the help of mathematical models and computers can be called quantitative investment.

In general, market research, fundamental analysis, stock selection, timing, and order placement can all be done automatically by the computer.

Characteristics of quantitative sun-type buried investment:

1. Objective results based on model calculations.

2. The core lies in the use of computer technology to mine investment laws from massive data.

3. Pay more attention to the breadth of research, screen the target of the whole market, and analyze the rental and combustion in multiple dimensions.

4. Diversified shareholdings and portfolio investment.

5. The model calculates the automatic order, and the transaction is disciplined.

Advantages of quantitative investing over subjective investing:

1. The scope of investment is more extensive. With the help of computer technology, quantitative investment collects more information with speed and breadth, and the scope of investment analysis covers a wider range, basically reaching the entire market.

2. Programmatic trading to avoid the influence of human subjective factors. It overcomes the weaknesses of human nature such as excessive self-confidence, greed, luck psychology, fear, and emotionality, and avoids the interference of subjective factors such as human emotions.

3. Rapid response to data processing to create transaction value. The computer is used for automatic analysis, with fast response speed, strong data processing ability and information mining ability, which can support high-frequency trading.

Regardless of whether the samples are uniformly quantified by equal interval stratification of continuous gray values or non-uniformly quantified by unequal interval stratification, all gray values between two quantization levels (known as the two decision levels) are represented by a single quantization value (called the quantization level of the quantizer output).

Homogeneous and non-homogeneous quantization.

According to the division of quantification levels, there are uniform quantization and non-uniform quantization.

Uniform quantization: The ADC input dynamic range is evenly divided into 2 N parts.

Non-uniform quantization: The dynamic range of the ADC input is not evenly divided, and is generally quantified by an exponential-like curve.

Non-uniform quantization is proposed for uniform quantization, because most of the general speech signals are small-amplitude signals, and the human ear hearing follows the exponential law. In order to ensure that the signal of interest can be reproduced more accurately, we should use more bits to represent small signals.

Common non-uniform quantizations include a-law and rate, etc., and the difference between them lies in the different quantization curves.

Scalar quantization and vector quantization.

According to the dimensionality of quantization, quantization is divided into scalar quantization and vector quantization. Scalar quantization is a one-dimensional quantization, with an amplitude corresponding to a quantified result. Vector quantization is two-dimensional or even multi-dimensional quantization, and two or more amplitudes determine a quantization result.

In the case of a two-dimensional case, for example, two amplitudes determine a point on a plane. This plane has been divided into n small regions according to probability in advance, and each region corresponds to an output result (codebook). The vectorquantizer will output the codeword corresponding to the region where the point determined by the input falls.

The advantage of vector quantization is that it introduces multiple factors that determine the output, and uses a probabilistic approach, which is generally more efficient than scalar quantization.