What is Monte Carlo simulation?
Answer: Monte Carlo simulation is a technique that allows people to run simulation many times to obtain numerical results or distribution of an unknown probabilistic entity. It was invented by Stanislaw Ulam in the late 1940s at the Los Alamos National Laboratory and was named after the Monte Carlo Casino where Ulam’s uncle often gambled [1].
Why is it used in analysis (generally)?
Answer: Monte Carlo simulation is a very flexible technique and could easily be adapted or extended. Usually, when it is difficult or, sometimes, even impossible to obtain a closed-form expression of certain results or attributes, it becomes very useful [1]. Because, through repeated random sampling, we might be able to obtain approximate values of our desired results or attributes.
What are its strengths and weaknesses (contrast with, say, the Design of Experiments)?
Answer: The strengths and weaknesses of Monte Carlo simulation contrasting with the Design of Experiments are listed below:
Strengths: Very flexible with very few limits to the analysis, able to handle empirical distributions, can be easily adapted and extended, very intuitive and easily understood, computationally tractable when the dimensions of uncertainty increase
Weaknesses: Not as well controlled as Designed of Experiments, usually requires more computing resources, and relies on large amount of repeated random sampling in order to obtain an approximate result
What is a random number generator and what properties should it have?
Answer: A random number generator is a computational device which is able to generate a sequence of numbers or symbols that lack any pattern and appear to be random [3]. Ideally, a good random number generator should b...
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... these parameters. We would be able to have a holistic view of the performance of this agent-based model over the feature space. In the meanwhile, we could identify the parameters which contribute most to variances in the output and identify the potential danger in certain feature space.
Typical analysis such as analytical analysis (PDE, ODE etc), numerical analysis (finite element analysis) might not be so attractive in the settings of my research problem above. One of the big disadvantages for typical analysis in my research problem is that typical analysis is computationally intractable due to the high dimensions in the above problems. Secondly, typical analysis is usually not as flexible and adaptable as Monte Carlo simulations. Some analytical analysis such as PDE does not even fit the setting of the above research problems, as it is empirical by its natural.
There were no significant error factors that may have affected the arrangement of the lab experiment. Everything went smoothly with relative ease.
...ways the case as many studies have failed to validate these systems, some revealing poor sensitivity, poor positive predictive value and low reproducibility (Gao et al 2007; Smith et al 2008; Subbe et al 2007; Jansen et al 2010).
There are many different factors to consider that play a part in experimental procedures. Without these variables, researchers would have a hard time making a claim about a particular topic, because they did not consider all sides of the experiment. An example of the variations done in experiments can be seen throughout Solomon Asch’s “Opinions and Social Pressure,”
2. What is the difference between a.. Describe the advantages and disadvantages of quasi-experiments. What is the fundamental weakness of a quasi-experimental design?
other weakness is that forensic anthropology does not involve an analysis of the DNA of a victim
Going into details of the article, I realized that the necessary information needed to evaluate the experimental procedures were not included. However, when conducting an experiment, the independent and dependent variable are to be studied before giving a final conclusion.
...ibition and control in "real life." Finally the sample sizes have been criticised, as have the ways in which the experimental and control groups were matched, making it impossible to know to what extent the differences observed reflected real differences between the different groups.
indicates towards a fraud. On eof the most important qualities or benefits of this model is that it understands the pattern in the data and generates the result. Once the result is generated the model checks as to how close was the result from the actual results. Based on this analysis the model adjusts its weights to give an accurate result the next time. Once this model has been trained to give accurate results, it can be used to analyze other data as well. Even when Neural Networks are widely accepted, they are not really used that much in the marketing industry merely by the fact that data preparation for this model is very complex time consuming as compared to the Regression Analysis. The marketers are much comfortable using the Regression Analysis over Neural Networks because of the ease of interpreting the results in the Regression Analysis.
Let us now see the quality of individual the population over the time. As shown below at the starting point of the algorithm individuals are of less quality. However as the time goes by population’s individuals are getting of higher quality and reaching the pick of global and local optima. The image below illustrate these stages of the algorithm.
There is also the potential of human error within this experiment for example finding the meniscus is important to get an accurate amount using the graduated pipettes and burettes. There is a possibility that at one point in the experiment a chemical was measured inaccurately affecting the results. To resolve this, the experiment should have been repeated three times.
Possible sources of error in this experiment include the inaccuracy of measurements, as correct measurements are vital for the experiment.
Often uses random sampling to select a large statistically representative sample from which generalizations can be drawn.
The normal distribution is very utilizable because of the central limit theorem, which states that, under mild conditions, the mean of many arbitrary variables independently drawn from the same distribution is distributed approximately customarily, irrespective of the form of the pristine distribution: physical quantities that are expected to be the sum of many independent processes (such as quantification errors) often have a distribution very proximate to the Gaussian. Moreover, many results and methods (such as propagation of dubiousness and least squares parameter fitting) can be derived analytically in explicit form when the germane variables are normally distributed.
3. Selecting a Sampling Technique: Selecting a sampling technique may require a little bit more time and may also involve several decisions, such as whether to use a Bayesian or traditional sampling approach, sample with or without replacement, and use non-probability or probability sampling
Experiments use inquiry skills and methods to make estimates, predictions, gather and analyze data, draw conclusions, and present findings. Examples include texting sink or float objects, growth conditions, and steps needed to create an electrical circuit.