# acts 9:1 22 sermon

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Using the transition probabilities, the steady-state probabilities indicate that 62.5% of weeks will be in a bull market, 31.25% of weeks will be in a bear market and 6.25% of weeks will be stagnant, since: A thorough development and many examples can be found in the on-line monograph Meyn & Tweedie 2005.[6]. It will be calculatedas: P({Dry, Dry, Rain, Rain}) = P(Rain|Rain) .P(Rain|Dry) . Solving this pair of simultaneous equations gives the steady state distribution: In conclusion, in the long term, about 83.3% of days are sunny. If (P)i j is the probability that, if a given day is of type i, it will be {\displaystyle {\dfrac {1}{6}},{\dfrac {1}{4}},{\dfrac {1}{2}},{\dfrac {3}{4}},{\dfrac {5}{6}}} The matrix P represents the weather model in which a sunny day is 90% I have presented one way of producing Markov chain diagrams here. So, a Markov chain is a discrete sequence of states, each drawn from a discrete state space (finite or not), and that follows the Markov property. This can be done by adding text between the curly brackets for each arrow. "rainy", and the rows can be labelled in the same order. t It doesn't depend on how things got to their current state. In the above-mentioned dice games, the only thing that matters is the current state of the board. A finite-state machine can be used as a representation of a Markov chain. To add the node for the Sunny state to our diagram we use: Here s is the label for our node. It is not necessary to know when they popped, so knowing To start, we will change the colors of the nodes. The weather on day 0 (today) is known to be sunny. X Example 1: A Simple Weather Model. The state option controls the appearance of the node. X The font used for the diagram can be changed to a sans-serif font by adding the font=\sffamily option to the \begin{tikzpicture} command. [1][2], The probabilities of weather conditions (modeled as either rainy or sunny), given the weather on the preceding day, Note that no options have been used here for either arrow. If you need a nice visual for a slideshow, video or website, you may consider some additional styling. This uses the standard cartesian coordinate system for the positioning of the nodes. This can be done by adding arrows that connect nodes to themselves. denotes the number of kernels which have popped up to time t, the problem can be defined as finding the number of kernels that will pop in some later time. You can use inline math for the labels as well if you want to use symbols or expressions. We will arrange the nodes in an equilateral triangle. Using the transition matrix it is possible to calculate, for example, the long-term fraction of weeks during which the market is stagnant, or the average number of weeks it will take to go from a stagnant to a bull market. The \draw command is used to add arrows to our Markov chain representing transitions between states. The final LaTeX source for this example can be found on Overleaf. The process described here is an approximation of a Poisson point process – Poisson processes are also Markov processes. This is in contrast to card games such as blackjack, where the cards represent a 'memory' of the past moves. 1 Adding text to a node is optional, although if you chose not to you must use empty curly brackets {} . 1 Let me know in the comments. 3 If one pops one hundred kernels of popcorn in an oven, each kernel popping at an independent exponentially-distributed time, then this would be a continuous-time Markov process. The loop above option draws the arrow above the state. This will draw arrows that bend to the right (from the perspective of the node at the starting end of the arrow) between the nodes. 4 The weather on day 2 (the day after tomorrow) can be predicted in the same way: In this example, predictions for the weather on more distant days are increasingly Theinitial probabilities for Rain state and Dry state be: P(Rain) = 0.4, P(Dry) =0.6 Thetransition probabilities for both the Rain and Dry state can be described as: P(Rain|Rain) = 0.3,P(Dry|Dry) = 0.8 P(Dry|Rain) = 0.7,P(Rain|Dry) = 0.2 . 0 The \draw command takes the form, where the start and end ids reference nodes previously defined. This article presents 3 examples along with the complete LaTeX source. Now,if we want to calculate the probability of a sequence of states, i.e.,{Dry,Dry,Rain,Rain}. A game of snakes and ladders or any other game whose moves are determined entirely by dice is a Markov chain, indeed, an absorbing Markov chain. This is represented by a vector in which the "sunny" entry is 100%, and the "rainy" entry is 0%: The weather on day 1 (tomorrow) can be predicted by: Thus, there is a 90% chance that day 1 will also be sunny. In the example above there are four states for the system. followed by a day of type j. To do this we will add an option to the arrows from sunny to rainy and rainy to sunny that controls where the labels are positioned relative to the arrows. 1 respectively. There are ways to define options that can be used for all nodes but I will wait until the next example to get into that. 1.1 An example and some interesting questions Example 1.1. X The final LaTeX source for this example can be found on Overleaf. The TikZ package is a great tool for generating publication quality illustrations of Markov chains in LaTeX. For an overview of Markov chains in general state space, see Markov chains on a measurable state space. The next state of the board depends on the current state, and the next roll of the dice. Are there other ways to do this? This guess is not improved by the added knowledge that you started with $10, then went up to$11, down to $10, up to$11, and then to \$12. The 2-state weather model is often used as a simple introductory model to Markov chains. In this example we will be creating a diagram of a three-state Markov chain where all states are connected. P(Dry) = 0.3 x 0.2 x 0.8 x 0.6 = 0.0288 A frog hops about on 7 lily pads. Are there easier ways of accomplishing what I have presented here? To see the difference, consider the probability for a certain event in the game. If you are interested in the details you can read the paper this model originates from here: Markov Aging Process and Phase-Type Law of Mortality. n The only thing one needs to know is the number of kernels that have popped prior to the time "t".