...I attended my first Deaf event on February 9th, 2017 in the Siverson Lounge here at Augustana University. The event title was ASL open house, and I attended from 5:30-7 P.M. Going into an experience like a Deaf event my nerves were running high for fear of the communication barrier that somewhat persists. I had previously learned from ASL-110 that the Deaf community is very welcoming to new signers, but my anxiety level does not seem to quite understand that concept. I feel much more comfortable signing and communicating with people who are on the same skill level that I am, but the requirements state otherwise so I branched out to find new people to communicate with. While attending the Deaf event I signed with a few old classmates that I had gotten to know fairly well from my interim class. But along with those friends, I signed to Brooke, an interpreting major, and Leroy, a Deaf person who lives here in Sioux Falls. I felt that it was much easier to understand Brooke because she was very animated with her signing. She told a group of us about her experiences as an interpreting major and how she took a level two class before a level one and it was the worst mistake of her...
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...ASL Deaf Event: Signmark Concert This past Thursday, I attended the Signmark concert hosted by Penn In-Hand. Signmark is a deaf Finnish rap artist that considers his music as being party hip hop that takes a stand. He is the first deaf artist to ever be signed to an international record label. There was a diverse group of ages, ethnicities, and gender that attended. The event was held at the Rotunda on 40th and Walnut along the center main stage with Signmark and his speaking interpreter/performer front and center. This made everyone be able to see them spatially and it reminded me of how our desks were assigned in a very concave like structure in class. There was a huge lit screen that also incorporated visual interaction with the audience that helped one identify some of Signmark’s signing. Throughout the performance I noticed many different levels of signing from both those hard at hearing and Deaf. There was a man at the event that would talk to the guests aloud but would only respond to those who signed back at him. He served as a personal liaison for my interacting and interpreting of Signmark’s lyrics throughout the show. When Signmark performed one of his favorite songs off his recent album Breaking the Rules, one motion that stood out was a much choreographed movement he would do to sign “winning/victory”. He motioned his dominant hand in a twist like positioning as if he was spinning his wrist in the air. Another sign that caught my attention was the signing “maniac”...
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...Silent dinner Deaf event at Culver’s. At this event, I signed with my friends; Lindsey, Cheyenne, Heather and a new girl I met from our class; Jayde. We took turns and signed everything on the worksheet describing our workplace, what our majors in college were and if we were doing anything for spring break. After that we reviewed some vocabulary from units 13 and 14 together and ate some dinner. I then went to sat with a man I know from work, his name is Gary. Gary goes to the day services at Opportunity Enterprises, where he works and is provided supported living. While I have never given any direct care to Gary I have seen him at Opportunity Enterprises when I’ve picked up and dropped off my clients. Gary and I signed for a bit, I asked him if he still goes to OE since I haven’t been there for awhile and he said yes, he also asked me if I would ever work with him, because he likes the fact that I know sign language and I said possibly, I’d have to ask my supervisor. I then tried to explain to him that since he isn’t a “24 hour” participant that it is hard for my supervisor to place me at his site, since there are other “24 hour” participants that need care at all times. He was disappointed, but agreed. I then talked with Professor and his wife for a while. His wife explained to me that she is a Sign Language interpreter and told me how to obtain such a license. I was interested and then told both Professor Maloney and his wife how I applied to Indiana Deaf camp to work...
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...other world. We attended the 16th Annual Deafestival held at the Los Angeles City Hall on Spring Street. I knew it would be a different experience for us; I was a little nervous, but when we arrived it hit me like a wave. I was so over whelmed with all that was going on around me. Yes, there are similarities to what I was use to, but it was nothing like have been to before. There was so much to take in with my eyes, and I automatically turned off my voice without even being aware of it until, Chris brought it to my attention when we got back to the car. As we walked through the crowds I realized that there are major differences between deaf events and "hearing" events, the crowds, the absences of color, the way the booths were set up, the abundance of information, and how different the people treated one another. It was very crowded, like a sea of deaf and hearing people. Conversations were going on everywhere, but it did not stop the crowds moving from one exhibit to another. Like waves, the crowds rolled over one another without missing anything. Unlike festivals I am used to that are full of color, fancy booths with flashing signs, and merchandise hung high; everything around me was very plain and low. There were tables, with white table clothes set up along the sidewalks, spread apart from one another. Some tables had canopies but nothing was enclosed. If I wanted to know what that exhibit was about I had to walk up to the table and pick up a pamphlet to read or ask the person...
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...Unit 2 DB Subjective Probability “ A probability derived from an individual's personal judgment about whether a specific outcome is likely to occur. Subjective probabilities contain no formal calculations and only reflect the subject's opinions and past experience.” (investopedia.com, 2013) There are three elements of a probability which combine to equal a result. There is the experiment ,the sample space and the event (Editorial board, 2012). In this case the class is the experiment because the process of attempting it will result in a grade which could vary from an A to F. The different grades that can be achieved in the class are the sample space. The event or outcome is the grade that will be received at the end of the experiment. I would like to achieve an “A” in this class but due to my lack of experience in statistical analysis, my hesitation towards advanced mathematics, and the length of time it takes for me to complete my course work a C in this class may be my best result. I have a 1/9 chance or probability to receive an “A” in the data range presented to me which is (A,A-,B,B-,C,C-,D,D- AND F). By the grades that have been posted I would say that the other students have a much better chance of receiving a better grade than mine. I have personally use subjective probability in my security guard business in bidding on contracts based on the clients involved , the rates that I charge versus the rates other companies charge and the amount of work involved...
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... Probability – the chance that an uncertain event will occur (always between 0 and 1) Impossible Event – an event that has no chance of occurring (probability = 0) Certain Event – an event that is sure to occur (probability = 1) Assessing Probability probability of occurrence= probability of occurrence based on a combination of an individual’s past experience, personal opinion, and analysis of a particular situation Events Simple event An event described by a single characteristic Joint event An event described by two or more characteristics Complement of an event A , All events that are not part of event A The Sample Space is the collection of all possible events Simple Probability refers to the probability of a simple event. Joint Probability refers to the probability of an occurrence of two or more events. ex. P(Jan. and Wed.) Mutually exclusive events is the Events that cannot occur simultaneously Example: Randomly choosing a day from 2010 A = day in January; B = day in February Events A and B are mutually exclusive Collectively exhaustive events One of the events must occur the set of events covers the entire sample space Computing Joint and Marginal Probabilities The probability of a joint event, A and B: Computing a marginal (or simple) probability: Probability is the numerical measure of the likelihood that an event will occur The probability of any event must be between 0 and 1, inclusively The sum of the...
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...= {-20, -19, …, -1, 0, 1, …, 19, 20} Number of people arriving at a bank in a day: S = {0, 1, 2, …} Inspection of parts till one defective part is found: S = {d, gd, ggd, gggd, …} Temperature of a place with a knowledge that it ranges between 10 degrees and 50 degrees: S = {any value between 10 to 50} Speed of a train at a given time, with no other additional information: S = {any value between 0 to infinity} 4 Sample Space (cont…) Discrete sample space: One that contains either finite or countable infinite set of outcomes • Out of the previous examples, which ones are discrete sample spaces??? Continuous sample space: One that contains an interval of real numbers. The interval can be either finite or infinite 5 Events A collection of certain sample points A subset of the sample space Denoted by ‘E’ Examples: • Getting an odd number in dice throwing experiment S = {1, 2, 3, 4,...
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...the stage where one can begin to use probabilistic ideas in statistical inference and modelling, and the study of stochastic processes. Probability axioms. Conditional probability and independence. Discrete random variables and their distributions. Continuous distributions. Joint distributions. Independence. Expectations. Mean, variance, covariance, correlation. Limiting distributions. The syllabus is as follows: 1. Basic notions of probability. Sample spaces, events, relative frequency, probability axioms. 2. Finite sample spaces. Methods of enumeration. Combinatorial probability. 3. Conditional probability. Theorem of total probability. Bayes theorem. 4. Independence of two events. Mutual independence of n events. Sampling with and without replacement. 5. Random variables. Univariate distributions - discrete, continuous, mixed. Standard distributions - hypergeometric, binomial, geometric, Poisson, uniform, normal, exponential. Probability mass function, density function, distribution function. Probabilities of events in terms of random variables. 6. Transformations of a single random variable. Mean, variance, median, quantiles. 7. Joint distribution of two random variables. Marginal and conditional distributions. Independence. iii iv 8. Covariance, correlation. Means and variances of linear functions of random variables. 9. Limiting distributions in the Binomial case. These course notes explain the naterial in the syllabus. They have been “fieldtested” on the class of 2000...
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...[pic] [pic] Markov Chain [pic] Bonus Malus Model [pic] [pic] This table justifies the matrix above: | | | |Next state | | | |State |Premium |0 Claims |1 Claim |2 Claims |[pic]Claims | |1 | |1 |2 |3 |4 | |2 | |1 |3 |4 |4 | |3 | |2 |4 |4 |4 | |4 | |3 |4 |4 |4 | | | | | | | | |P11 |P12 |P13 |P14 | | | |P21 |P22 |P23 |P24 | | | |P31 |P32 |P33 |P34 | | | |P41 |P42 |P43 |P44 | | | | ...
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...Permutations The word ‘coincidence’ is defined as an event that might have been arranged though it was accidental in actuality. Most of us perceive life as a set of coincidences that lead us to pre-destined conclusions despite believing in a being who is free from the shackles of time and space. The question is that a being, for whom time and space would be nothing more than two more dimensions, wouldn’t it be rather disparaging to throw events out randomly and witness how the history unfolds (as a mere spectator)? Did He really arrange the events such that there is nothing accidental about their occurrence? Or are all the lives of all the living beings merely a result of a set of events that unfolded one after another without there being a chronological order? To arrive at satisfactory answers to above questions we must steer this discourse towards the concept of conditional probability. That is the chance of something to happen given that an event has already happened. Though, the prior event need not to be related to the succeeding one but must be essential for it occurrence. Our minds as I believe are evolved enough to analyze a story and identify the point in time where the story has originated or the set of events that must have happened to ensure the specific conclusion of the story. To simplify the conundrum let us assume a hypothetical scenario where a man just became a pioneer in the field of actuarial science. Imagine him telling us his story in reverse. “I became...
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...Employees in Communicating to Mute and Deaf Passengers A Study on the Challenges of Airport Employees in Communicating Mute and Deaf Passengers Submitted By: Dela Vega, Joaquin Jr. M AB- 3-1 Submitted to: Professor Ivan Mascardo August 28, 2015 Chapter 1 1.1 Introduction In 1951, ICAO announced that English will be the compulsory language used for the international aviation. (Varavudhi, 2009). Such requirements would improve communication, coordination, and performance of the required safety and related duties of airport employees. “Communication is the exchange and flow of information and thoughts from one person to another; it involves a sender transmitting a thought, information, or feeling to a receiver. Communication includes writing and talking, as well as nonverbal communication (such as facial expressions, body language, or gestures), visual communication (the use of images or pictures, such as painting, photography, video or film) and electronic communication (telephone calls, electronic mail, - cable television, or satellite broadcasts).(Mallett-Hamer, 2005, p.1). A successful communication demand same basic ability of sender and receiver. Here ideas are delivered, which received and an immediate feedback generated” (kabir, n.d. p.1) People who are deaf and mute may have challenges communicating with others via spoken words and may have challenges being aware of audio events in their environments. This is...
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...Erastus “Deaf” Smith was born April 19, 1787 in Dutchess County, New York. Erastus’ nickname was “Deaf” Smith also known as “El Sordo.” Erastus then moved to Port Gibson, Mississippi in 1798. Then he came to Texas in 1821 for health reasons, but returned back to Natchez in 1822. Erastus appeared in most events leading up to Texas Independence. His mother was Mary Smith and his father was Chilab Smith they were very religious they were strict Baptist. When Erastus was 11 or 12 he moved with his family to Natchez, Mississippi Territory. Later on a childhood disease caused Erastus to lose his hearing. Erastus married Guadalupe Ruiz de Duran December 12, 1797. Guadalupe was a widow of Jose Maria Vicente Duran, by whom had three children; Rufugia, Josefa, and Lucinda. The Smiths had four children; Susan Concepcion, Gertrudes, Travis, and Simona. Together they had a total of seven children, four of them were Erastus’. After Erastus helped at the Battle of San Jacinto he returned to Columbia and he then later moved to Richmond in Fort Bend County. Erastus’ education came from his parents and nature. He didn’t have the same education as most kids his age. Most of what Erastus’ knew was common sense. There wasn’t much about his education but I think that teaching him was difficult for him and his...
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...presence with probability 0.99. If it is not present, the radar falsely registers an aircraft presence with probability 0.10. We assume that an aircraft is present with probability 0.05. What is the probability of false alarm (a false indication of aircraft presence), and the probability of missed detection (nothing registers, even though an aircraft is present)? A sequential representation of the sample space is appropriate here, as shown in Fig. 1. Figure 1: Sequential description of the sample space for the radar detection problem Solution: Let A and B be the events A={an aircraft is present}, B={the radar registers an aircraft presence}, and consider also their complements Ac={an aircraft is not present}, Bc={the radar does not register an aircraft presence}. The given probabilities are recorded along the corresponding branches of the tree describing the sample space, as shown in Fig. 1. Each event of interest corresponds to a leaf of the tree and its probability is equal to the product of the probabilities associated with the branches in a path from the root to the corresponding leaf. The desired probabilities of false alarm and missed detection are P(false alarm)=P(Ac∩B)=P(Ac)P(B|Ac)=0.95∙0.10=0.095, P(missed detection)=P(A∩Bc)=P(A)P(Bc|A)=0.05∙0.01=0.0005. Application of Bayes` rule in this problem. We are given that P(A)=0.05, P(B|A)=0.99, P(B|Ac)=0.1. Applying Bayes’ rule, with A1=A and A2=Ac, we obtain P(aircraft present | radar registers) =...
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...1.M/G/ Queue a. Show that Let A(t) : Number of arrivals between time (0, t] “ n should be equal to or great than k” since if n is less than k (n<k), Pk(t)=0 Let’s think some customer C, Let’s find P{C arrived at time x and in service at time t | x=(0,t)] } P{C arrives in (x, x+dx] | C arrives in (0, t] }P{C is in service | C arrives at x, and x = (0,t] } Since theorem of Poisson Process, The theorem is that Given that N(t) =n, the n arrival times S1, S2, …Sn have the same distribution as the order statistics corresponding to n independent random variables uniformly distributed on the interval (0, t) Thus, P{C is in service | C arrives between time (0, t] } Since let y=t-x, x=0 → y=t, x=t →y=o, dy=-dx Therefore, In conclusion, ------ (1) 1-a Solution Since b. let 1-b Solution ------------------------------------------------- 2. notation Page 147 in “Fundamentals of Queuing Theory –Third Edition- , Donald Gross Carl M. Harris a. b. ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- 3. a. let X=service time (Random variable) and XT=total service time (Random variable) X2=X+X, X3=X+X+X, ….. f2(x2)...
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...Probability & Mathematical Statistics | “The frequency concept of Probability” | [Type the author name] | What is probability & Mathematical Statistics? It is the mathematical machinery necessary to answer questions about uncertain events. Where scientists, engineers and so forth need to make results and findings to these uncertain events precise... Random experiment “A random experiment is an experiment, trial, or observation that can be repeated numerous times under the same conditions... It must in no way be affected by any previous outcome and cannot be predicted with certainty.” i.e. it is uncertain (we don’t know ahead of time what the answer will be) and repeatable (ideally).The sample space is the set containing all possible outcomes from a random experiment. Often called S. (In set theory this is usually called U, but it’s the same thing) Discrete probability Finite Probability This is where there are only finitely many possible outcomes. Moreover, many of these outcomes will mostly be where all the outcomes are equally likely, that is, uniform finite probability. An example of such a thing is where a fair cubical die is tossed. It will come up with one of the six outcomes 1, 2, 3, 4, 5, or 6, and each with the same probability. Another example is where a fair coin is flipped. It will come up with one of the two outcomes H or T. Terminology and notation. We’ll call the tossing of a die a trial or an experiment. Where we...
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