Fluent Essays

Please go through the uploaded document and write responses to the other students initial posts. week_3_responses.docx Unformatted Attachment Preview Course: Business Intelligence Initial Post 1: Naïve Bayes allows us to compute the conditional distribution of a set of values and to compute the posterior probability of the limited distribution, but this is only possible if the set of values is small, say if the limited distribution in question is a Monoid. Since we do not have a critical use case for regular shipping, think of Naïve Bayes as being like a particular case of Bayes Networks, where the computations are linear rather than logistic. The Monoid constraint tells us that Naïve Bayes does not have an orthogonal violation; on the other hand, as it only calculates probabilities, Naïve Bayes has an orthogonal violation since it (Chu, Shyr, & Zhong, 2020). Experience with deriving dense assemblies of cellular automata and artificial networks, we understand the meaning of the above in the context of, for example, order-to-magnitude visualized morphological measures of physiological parameters of biofilm formation in a cell culture dish. These networks are certainly more complicated than mixtures of random variants. However, they are still formed with a discrete decision: random elements are always removed before generating a dense network with a large number of activations. The process for developing Bayesian network models follows a well-defined procedure. First, to define the parameters for each observation to solve the task, which can be done either using discrete-time regressions or multivariate normal, but the resulting model needs to be generative so that the network distribution grows exponentially with time. First, we need to understand how a neural network works. Before we can model it, we need to learn how it works. Fortunately, the steps necessary for this are mostly natural. I can find the code for this model on (Github Ramirez-Angulo, & Guevara, 2020). There are many different types of Neural Networks so that different word representations can represent them. We can use word vectors to serve all of these word representations in our model. To generate a Bayesian network model, we need to process the data one way or another, and as this happens, different models will emerge based on our decision making. Our work on the assignment should take us all the way there to be able to pick which model will have the most likely fit for our dataset. The design of Bayesian network models is intrinsically complicated. The models themselves need to be introduced, fine-tuned, reviewed, and stable over time. Here, we provide a practical introduction to Bayesian networks and support for Bayesian network development with actual data and real-time simulations (Tripathi, & Bagga, 2020). Initial Post 2: Bayesian Networks are incredible information representation and reasoning mechanisms. Bayesian Networks speak to occasions and causal relationships between them as conditional probabilities, including random variables. Given the values of a subset of these variables (proof variables), Bayesian Networks can figure the chances of another subset of variables (question variables). Bayesian Networks can be created automatically (learned) by utilizing statistical data (examples) (Remco, 2007). The notable Machine Learning algorithm, Naïve Bayes, is a particular case of a Bayesian Network. Naive Bayes and Bayesian networks are probabilistic graphical models utilized for modeling large datasets, including heaps of uncertainties among its various reliant feature sets. The absolute most typical applications of these models are image segmentation, medical diagnosis, and various other data bunching and data classification applications. A classification issue deals with recognizing to which category a particular instance has a place with, based on past information acquired by analysis of various such situations. The cases are portrayed utilizing a lot of variables called attributes or features. A Naive Bayes model assumes that all the characteristics of an instance are autonomous of each other, given the class of that instance (Markov, and Russell, 2007). This is an exceptionally straightforward representation of the framework. However, the autonomy assumptions made in this model are erroneous and unrealistic. The Bayesian system model enhances the Naive Bayes model by adding one progressively level of interaction among attributes of the framework. In the Bayesian system model, each quality is subject to its class and one other attribute from the feature set. Since this model incorporates the conditions among the characteristics, it is more realistic than a Naive Bayes model. Bayesian Networks are probabilistic induction motors that help reasoning under uncertainty. The article by Lauría (2005) presents a technique for building an information technology implementation Bayesian Networks from customer server overview data. Most research on software venture hazard analysis centers around the revelation of correlations between chance factors and task results. At present, concentrates on Bayesian Networks - based hazard analysis of software ventures includes two ways of system development: Specialists manually indicate the system to reflect master information, and (2) automatically learn the system from observational data. Since the manual technique isnt based on observational data, it will undoubtedly contain a masters emotional bias. Along these lines, more attention is given to the ways of Bayesian Networks automatic learning based on observational data. Indeed, even with the integration of master information as structural constraints, a few challenges despite everything exist in recognizing correlation and causality in the final system. In software venture chance management, relationship and causality are often utilized mistakenly for each other; causality analysis has never been given adequate attention. Master information can rearrange the search for a Bayesian Networks structure, speaking to a given domain with improved forecast accuracy. Lauría, (2005) demonstrated how to incorporate existing master information (or aptitude) as system structural constraints in the process of automatic learning of IT implementation Bayesian Networks from real data. They built up a reasonable model, yet their system cannot recognize causality from correlation, which adds to the trouble of immediate and viable hazard planning. Instructions: • Need 2 Responses • Each response minimum of 200 words • Each Response need a minimum of 2 APA References Course: Analyzing and Visualizing Initial Post 1: An important aspect of report development is to understand the users requirements thoroughly. The flow of Visualization Development: • Understanding Users Requirement • They are confirming if data is available and data source type (website, CSV, REST API, database). • Integration data if not readily available • Starting Development work with initial proof of concept to align users expectations. • Once approval from users, doing full-fledged development. • They are making the report available for the users by implementing Role-Based Security if needed. All these steps are an iterative process for Visualization development. Initial Post 2: I did design a graph similar to this one during my undergraduate years. I did use the following workflow. At the time of this project, I did not know data visualization and the techniques highlighted in the Textbook, and so the approach I used is very different from what Kirk has noted. First, I did some research on the topic and understood the topic of discussion. I then came up with a hypothesis. To find data to prove my hypothesis, I did perform some experiments to gather some data. Once the data was obtained, deciding on the visualization tool to use to represent the data was not difficult because I knew the kind of relationship to expect in advance; hence a simple linear graph was appropriate. The choice of the scale was made based on the data I obtained. The goal was to keep this as simple as possible for me. The audience was not a factor for making this choice. However, if I were to do the same thing project again, I would consider the audience. Because depending on the audience, a specific graph may be more appealing than another. Initial Post 3: Today, visual data representation has become a powerful data communication tool. Data visual representation is displaying data in graphs, charts, among other visualization tools (Data Visualization., 2020). It involves the transformation of figures and raw data into visual objects. This will help the non-specialist understand the interpretation of findings easily. The following graph is a data representation of the time served as the headteacher in an individual school. This represents the results of the survey conducted in 19 schools to ascertain the duration of current heads of the institution served in the same capacity. Time served as head teacher 40.0\% 35.0\% 36.8\% 31.6\% 30.0\% 25.0\% 20.0\% 15.8\% 15.8\% 4-6 years 7-9 years 15.0\% 10.0\% 5.0\% 0.0\% Below 3 years Above 10 years According to the above representation, most of the respondent Headteachers (36.8\%) have been in their current workstation for over ten (10) years. Headteachers who have been in their current work station for less than three years are 31.6\%, while those who have been in their current workstation for between 4-6 years and 7-9 years are 15.8\%, respectively. The survey was conducted to know the heads of an institution who have served for more years. This shows that most of them were experienced persons on matters of education, and so their responses were highly reputable. This representation is a graphical representation. Most of the people who what to represent their data and figures findings, they tend to prefer this kind of visual representation. This is because it can be understood easily by non-specialist or the audience since graphs give clues which words and equations cannot. Therefore, it is among the best data, visual representation tools. Lastly, the audience were all the teachers. In choosing the kind of visual data representation, it is essential to consider the audience. The audience plays a vital role in determining the visual data representation. Therefore, before choosing the kind of visual representation of data, it is good to consider the audience. Initial Post 4: I have made a full data visualization about different themes every month and expound on the procedure of data gathering, preparing, drawing ideas, and coding it into the real world. I am in my second year as Im composing this, and Ive learned tons and tons more since the beginning, both on a specialized and innovative front. Just nothing beats doing projects on the off chance that you need to learn data visualization. Screenshots with results from the first year of data sketches Instructions: • Need 4 Responses • Each Response minimum of 150 words • Each Response need a minimum of 1 APA Reference ... Purchase answer to see full attachment