### 统计代写|机器学习作业代写machine learning代考| General Systems Theory

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|机器学习作业代写machine learning代考|General Systems Theory

Von Bertalanffy introduced General System Theory in 1956 . Building on Cybernetic theory, Von Bertalanffy defines a system as “a complex of interacting elements.” He also contemplated the idea of thinking systems across all disciplines to discover broad principles that are valid in connection with all systems. The concept of “system” was introduced as a new scientific paradigm (which characterized classical science) and was related to the contrast between the mechanical and analytical paradigm. A notion of paramount importance concerning the general systems theory is the focus it places on interactions. The center within relationships indicates that a single autonomous element’s behavior is unlike its behavior when other elements engage in interaction with the aforementioned element. The differentiation between closed, open, and isolated systems represents another fundamental principle. Within systems that are open, exchanges of matter, energy, information and people occur with the external environment. Within closed systems, the only exchanges that take place are those which involve energy. Systems that are isolated are characterized by the complete lack of exchanges of elements. Based on these fundamental concepts, diverse approaches began to develop as a result of the emergence of the general systems theory. These include open system theory, the viable system approaches, and the viable system models. The open system theory (OST) is concerned with the examination of the relationships between different organizations and the environments that they are a part of (Mele et al. 2010).

Bertalanffy’s approach to analyzing complex systems emphasizes key concepts such as embeddedness within other larger systems, dynamic processes of selforganization, growth, and adaptation. Bertalanffy adopts a holistic approach to his analysis of living systems in stark contrast to the conventional and widely accepted reductionist view on complex phenomena prevalent during the earlier parts of the twentieth century. The reductionist philosophical stance attempts to interpret complex systems in a gestalt state as the sum of all parts or components, while General Systems Theory looks at complex systems holistically, whereby the whole is bigger than the sum of its parts. Systems that learn and adapt must engage successfully with their environments to maintain growth and their ability to adapt. Within such a dynamic relationship, certain systems exist for the sole purpose of supporting the effective functioning of other systems, thereby preventing their failure. Bertalanffy’s work is widely recognized for its universality and application beyond its original focus on theoretical biology and cybernetics, to also include fields as diverse as sociology, economics, statistical analysis, ecology, meteorology, political science and psychology. Systems theory allows us to apply a common framework for the analysis and holistic understanding of complex phenomena and systems. As such, systems theory enables us to better understand individual components and subsystems in the context of their relationship to each other, as well as to other systems and their environment as a higher scale complex system.

## 统计代写|机器学习作业代写machine learning代考|The Theory of Autopoiesis

The term autopoiesis is derived from the Greek words auto, meaning “self” and poiesis, meaning “creation.” The theory of autopoiesis was proposed by biologists Humberto Maturana and Francisco Varela in 1972 in their publication Autopoiesis and Cognition: The Realization of the Living, where they introduce their theory to describe the essential processes and characteristics that are fundamental for all living organisms. Autopoietic systems consist of self-creating processes that produce all components and subcomponents necessary to sustain its existence as a living entity.
Networks represent relationships between various components which are selfreferential and generate the complexity that characterizes living organisms. These types of processes serve an identical function in the human body (i.e., a cell) as in the mind (i.e., cognition). Different components are incorporated in networks which are self-creating since they produce other components to sustain themselves as well as the structure in its whole complexity. In contrast, a system that harnesses the energy to generate complexity that is uncharacteristic of the system itself, is called “allopoiesis” – an example in that respect would be a factory assembly line whose elements manufacture external products, not to perpetuate itself. Varela and Maturana assert that the organism’s primary function could be described as part of the nature of the self-referential as well as being encapsulated in the selfcreating networks and processes. Pursuing a high-level understanding of neural networks, Maturana proposes that a similar process applies (Maturana and Varela 1980). Maturana ascertain that cognition represents a system governed by selfreferencing, whereby understanding is shaped by previous understanding. What is grasped by the human mind through what the eyes perceive does not constitute the all-inclusive reality of the “outside”; rather, it is the mere articulation of the brain’s neural networks that brings to light the experience of understanding and interpreting (Geyer 2001).

It is perhaps best understood in contrast to an allopoietic system, such as a factory, which takes in materials and uses them to produce something other than itself. Damiano and Luisi $(2010)$, p.148 argue that artificial or completely abstract systems can also exhibit autopoiesis, however, to be considered “living, or alive” they must have cognitive capabilities.

The authors summarize three critical conditions and dimensions of autopoiesis as:

1. REACTION NETWORK: Self-production, self-organization, and selfmaintenance. These properties are exhibited through a “regenerative network of processes which takes place within a boundary of its own making and regenerates itself through cognitive or adaptive interactions with the medium.”
2. BOUNDARY: Defined by a semi-permeable boundary which allows for exchange with the system’s external environment. The components of the boundary are being produced by a network of reactions which takes place within the boundary. The network of reactions is generated by conditions produced by the existence of the boundary itself.
3. COGNITION: The adaptive interaction of a living system with its environment.

## 统计代写|机器学习作业代写machine learning代考|Structural Determinism

Structural determinism is the notion that form follows function. The structure of a system will determine its behavior, actions and evolutionary development. Whether we are exploring biological, artificial or abstract systems (e.g., legal,religious), structure (form) follows function. Systems adapt their form through the continuous pursuit of specific functions. Any changes that can occur to the structure of an autopoietic system must never interfere with the process of autopoiesis (self-regulation and self-reproduction), or it simply would not exist. Environmental forces could only “trigger or select” possibilities that the system’s structure makes available at any given time. The structure (the actual components and their relationships) may change dramatically over time or may be realized in many ways so long as the organization maintains its process of self-production. It can be said to be organizationally closed but structurally open (Mingers 1991, p. 280).

Structural coupling is the idea that autopoietic systems can become structurally coupled to other systems and their environments through a process that Maturana calls “evolutionary drift” or “mutual specification.” Since the system produces itself, it gains a significant degree of autonomy-it depends less on other entities for its continual existence. Simultaneously, if it ever fails to produce that which is necessary, then autopoiesis must break down, and the entity will disintegrate. If no functionalism is involved, however, the system either continuously maintains autopoiesis or does not (Mingers 1991, p. 280).

Sociologist Niklas Luhmann borrows the concept of autopoiesis and uses it to describe society as a complex autopoietic system. The social system, according to Luhmann, consists of communications which produce subsequent communications based on existing social structures. For instance, social conventions structure how to respond to questions, orders and statements. Such conventions, however, can never exhaustively determine the subsequent communications so that there is always an element of contingency involved in the system. That is, the system’s complexity entails that it must select some communications over others, i.e., the system must necessarily reduce complexity. The first and foremost way in which the social system reduces complexity is by drawing a boundary or distinction between itself and its outside, between the system and its environment. After that, the system can draw distinctions (i.e., communicate) on the system side of that distinction to increase its internal complexity. In this way, the social system is autopoietic. It generates its own elements as well as its own boundaries. Luhmann defines social systems not as a set of actions and functions but as the sum of all the information exchange taking place between all systems and subsystems (individual functioning systems such as the economy, science, politics, media, etc.). According to Luhmann’s view, all subsystems emerge from social systems, and as functions become increasingly differentiated, they achieve their own operational closure and autopoiesis. John Mingers (1991) examines the three elements of autopoietic systems (structural coupling, structural determinism, and boundary) within a social organizational perspective to determine the extent to which the original criteria and definitions of Maturana and Varela can be observed within a social context.

## 统计代写|机器学习作业代写machine learning代考|General Systems Theory

Von Bertalanffy 于 1956 年介绍了一般系统理论。基于控制论理论，Von Bertalanffy 将系统定义为“相互作用元素的复合体”。他还考虑了跨学科思考系统的想法，以发现适用于所有系统的广泛原则。“系统”的概念作为一种新的科学范式（以经典科学为特征）被引入，并与机械范式和分析范式之间的对比有关。关于一般系统理论的一个最重要的概念是它对相互作用的关注。关系中的中心表示单个自治元素的行为不同于其他元素与上述元素进行交互时的行为。封闭式、开放式、孤立系统代表了另一个基本原则。在开放的系统中，物质、能量、信息和人的交换与外部环境发生。在封闭系统中，唯一发生的交换是那些涉及能量的交换。孤立的系统的特点是完全不交换元素。基于这些基本概念，随着一般系统理论的出现，各种方法开始发展。这些包括开放系统理论、可行系统方法和可行系统模型。开放系统理论 (OST) 关注不同组织之间的关系及其所属环境的检查（Mele et al. 2010）。物质、能量、信息和人的交换与外部环境发生。在封闭系统中，唯一发生的交换是那些涉及能量的交换。孤立的系统的特点是完全不交换元素。基于这些基本概念，随着一般系统理论的出现，各种方法开始发展。这些包括开放系统理论、可行系统方法和可行系统模型。开放系统理论 (OST) 关注不同组织之间的关系及其所属环境的检查（Mele et al. 2010）。物质、能量、信息和人的交换与外部环境发生。在封闭系统中，唯一发生的交换是那些涉及能量的交换。孤立的系统的特点是完全不交换元素。基于这些基本概念，随着一般系统理论的出现，各种方法开始发展。这些包括开放系统理论、可行系统方法和可行系统模型。开放系统理论 (OST) 关注不同组织之间的关系及其所属环境的检查（Mele et al. 2010）。孤立的系统的特点是完全不交换元素。基于这些基本概念，随着一般系统理论的出现，各种方法开始发展。这些包括开放系统理论、可行系统方法和可行系统模型。开放系统理论 (OST) 关注不同组织之间的关系及其所属环境的检查（Mele et al. 2010）。孤立的系统的特点是完全不交换元素。基于这些基本概念，随着一般系统理论的出现，各种方法开始发展。这些包括开放系统理论、可行系统方法和可行系统模型。开放系统理论 (OST) 关注不同组织之间的关系及其所属环境的检查（Mele et al. 2010）。

Bertalanffy 分析复杂系统的方法强调关键概念，例如嵌入其他更大系统、自组织、增长和适应的动态过程。Bertalanffy 采用整体方法分析生命系统，这与 20 世纪早期流行的关于复杂现象的传统和广泛接受的还原论观点形成鲜明对比。还原论的哲学立场试图将格式塔状态下的复杂系统解释为所有部分或组成部分的总和，而一般系统理论则从整体上看待复杂系统，即整体大于部分之和。学习和适应的系统必须成功地融入环境，以保持增长和适应能力。在这样的动态关系中，某些系统存在的唯一目的是支持其他系统的有效运行，从而防止它们发生故障。Bertalanffy 的工作因其普遍性和应用性而广受认可，超越了最初对理论生物学和控制论的关注，还包括社会学、经济学、统计分析、生态学、气象学、政治学和心理学等多个领域。系统理论使我们能够应用一个通用框架来分析和全面理解复杂的现象和系统。因此，系统理论使我们能够更好地理解各个组件和子系统在它们相互关系的背景下，以及与其他系统及其环境作为更高规模的复杂系统的关系。从而防止他们的失败。Bertalanffy 的工作因其普遍性和应用性而广受认可，超越了最初对理论生物学和控制论的关注，还包括社会学、经济学、统计分析、生态学、气象学、政治学和心理学等多个领域。系统理论使我们能够应用一个通用框架来分析和全面理解复杂的现象和系统。因此，系统理论使我们能够更好地理解各个组件和子系统在它们相互关系的背景下，以及与其他系统及其环境作为更高规模的复杂系统的关系。从而防止他们的失败。Bertalanffy 的工作因其普遍性和应用性而广受认可，超越了最初对理论生物学和控制论的关注，还包括社会学、经济学、统计分析、生态学、气象学、政治学和心理学等多个领域。系统理论使我们能够应用一个通用框架来分析和全面理解复杂的现象和系统。因此，系统理论使我们能够更好地理解各个组件和子系统在它们相互关系的背景下，以及与其他系统及其环境作为更高规模的复杂系统的关系。还包括社会学、经济学、统计分析、生态学、气象学、政治学和心理学等多种领域。系统理论使我们能够应用一个通用框架来分析和全面理解复杂的现象和系统。因此，系统理论使我们能够更好地理解各个组件和子系统在它们相互关系的背景下，以及与其他系统及其环境作为更高规模的复杂系统的关系。还包括社会学、经济学、统计分析、生态学、气象学、政治学和心理学等多种领域。系统理论使我们能够应用一个通用框架来分析和全面理解复杂的现象和系统。因此，系统理论使我们能够更好地理解各个组件和子系统在它们相互关系的背景下，以及与其他系统及其环境作为更高规模的复杂系统的关系。

## 统计代写|机器学习作业代写machine learning代考|The Theory of Autopoiesis

1. 反应网络：自我生产、自我组织和自我维护。这些特性通过“过程的再生网络表现出来，该网络发生在其自身制造的边界内，并通过与媒介的认知或适应性相互作用来再生自身。”
2. 边界：由允许与系统外部环境交换的半渗透边界定义。边界的组成部分是由边界内发生的反应网络产生的。反应网络是由边界本身的存在所产生的条件产生的。
3. 认知：生命系统与其环境的适应性相互作用。

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。