Questions tagged [markov-process]

A Markov process is a stochastic process for which the Markov property holds: If you know the current state, then the next state is independent of all past states.

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How to deal with multiple possible rewards in a transition in MDP

Suppose there is a state S with two transitions under action A but both transited states are ...
1 vote
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64 views

How to set the parameters of a Hidden Markov Model that'll be used to correct the mistakes by a previous classifier?

Say we've previously used a neural network or some other classifier C with $N$ training samples $I:=\{I_1,...I_N\}$ (that has a sequence or context, but is ignored by C) the, belonging to $K$ classes. ...
2 votes
1 answer
247 views

How to use HMMs for continuous value prediction

I have some time-series data, which I need to use to predict a continuous value for a given time-stamp. I was initially doing it using a Multivariate Regression Model but I later figured that a time-...
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memory error- python N-th order Markovian transition matrix from a given sequence

Ok. What is wrong with you code! I am trying to calculate transition probabilities for each leg. The code works for small array but for the actual dataset I got memory error. I have 64 g version ...
2 votes
2 answers
605 views

What are the differences between Reinforcement Learning (RL) and Supervised Learning?

What is the difference between Reinforcement Learning (RL) and Supervised Learning? Does RL hava more difficulty in finding a stable solution? Does Q-learning have more difficulty in finding a ...
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Using Markov Chains in digital customer journey

I have data of each page visited by a customer in a session, my objective is to find out the most optimal path where we see the maximum conversion rate. My idea Is to use Markov Chain to identify that ...
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2 answers
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Simple Markov Chains Memoryless Property Question

I have a sequential data from time T1 to T6. The rows contain the sequence of states for 50 customers. There are only 3 states ...
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If I use Gibbs sampling with a Bayesian model, what do I have to check is memoryless?

Right now I am trying to better understand how Bayesian modeling works with just the basics. I found through reading tutorials that some very basic Bayesian models like Bayesian Hierarchical Modeling ...
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Compute for policy the state value function v(s) for each state

The instruction of the question: State A is absorbing. Transition to A from state 1 or 4 yields an immediate reward of 12. All other transitions incur a reward of 1. Transitions are deterministic (i.e....
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Markov Process and transition matrix

I would like to find some good courses but also a quick response on how to model transition matrix given the states. Imagine having 4 states and the following array [1,2,4,1,3,4,2 etc etc]. What ...
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2 answers
93 views

Machine Learning algorithm for detecting anomalies in large sets of events

Let's start with the following hypothetical preconditions: There is traffic: normal and anomaly. Each traffic sample contains a list of events (of variable size) Events happen in order, the possible ...
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Method for predicting future state, based on time spent in previous states

So what I'm looking for is the best approach to predict a future state. Say we have three states: A, B, C. I want to predict if in the next time-interval (f.e. a day or a week) the state will become C....
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Value function when the policy is deterministic

This is the value function expression for a stochastic policy: $\displaystyle v_{\pi}(s)=\sum_{a \in \mathcal{A}}\pi(a|s)\bigg(\mathcal{R}_s^a+\gamma \sum_{s' \in \mathcal{S}} \mathbb{P}_{ss'}^a v_{\...
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Jacks car rental problem: why deterministic policies?

In Sutton & Barto Book: Reinforcement Learning: An Introduction, there is the following problem: I have this question: why are the policies to be considered here are deterministic?
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Can Reinforcement Learning learn to be deceptive?

I have seen several exampled of deploying RL agents in deceptive environnement or games and the agent learns to perform its tasks regardless. What about the other way around? Can RL be used to create ...
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Computing the state-value function of a Markov decision process from the classical definition

For the above Markov decision process under given action policy $a_1$, how can I determine the value of state $s_1$ using the state-value definition $v(s)=E[G_t| S_t=s]$ where $G_t$ is the return? ...
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Find changes in variables into two states

I have a dataframe like this: ...
7 votes
2 answers
981 views

Reward dependent on (state, action) versus (state, action, successor state)

I am studying reinforcement learning and I am working methodically through Sutton and Barto's book plus David Silver's lectures. I have noticed a minor difference in how the Markov Decision Processes ...
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3 answers
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How do I choose a discount factor in Markov Decision Problems?

I'm referring to the gamma in the Value function:
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1 answer
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Suggestions for studying *Clickstream* data

I've essentially been handed a dataset of website access history and I'm trying to draw some conclusions from it. The data supplied gives me the web URL, the datetime for when it was accessed, an the ...
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1 answer
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Artificially increasing frequency weight of word ending characters in word building

I have a database of letter pair bigrams. For example: ...
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1 answer
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Markov Chain vs Bayes Net

I am learning about Markov Chain and Bayesian Nets. However at this point I am a bit confused about what types of problems are modelled with the two different models presented to us. From what I ...
2 votes
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"Learning" algorithm to use when future depends on past events (MDP property not met)

There are around 5 different retirement plans available in my country. People can pick from them freely. I would like to create a solution that would try to predict the best plan(s) given a particular ...
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1 answer
184 views

Difference between $Q(s,a)$ ,$V^*(s)$ and $V^\pi(s)$ in Markov Decision Process?

I am new to RL and I am trying to understand how to find solutions of an MDP. This is what I understand so far -> since the nature of our environment is stochastic, at a state 's' if we take an ...
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global independence vs local independence in markov network

I could not understand the local independence and global independence of a markov network. Please help me understand with a simple graph
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Hidden Markov Model with Autoregressive emission model?

So far, all standard HMM implementations I've seen assume some variation of a Gaussian Mixture (GMM) as their emission model. It can of course only have a single mixture component which reduces it to ...
2 votes
1 answer
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Markov Decision Process representation

I'm attempting to model a simple process using a Markov Decision Process. Let $A$ be a set of $3$ actions : $ A \in \{b,s\}$. $T(s,a,s')$ represents the probability of if in state $s$ , take action $...
1 vote
2 answers
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Choosing a model for input: categorised, weighted sequence, output: binary variable

What would be an appropriate model for predicting a binary target variable, given a weighted sequence? Sequences will be reasonably short, typically between ~ 1 and 5 elements. Illustrated example Say ...
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state transition classification on terminal state

I have data on a unit $i$ which enters an entry state $S_0$. This unit has some covariates $x_i$ I would like to predict the probability the unit will reach the terminal state $S_{pos}$ or $S_{neg}$. ...
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How do I test and validate a Markov Model in RStudio?

I've created a Markov Model in RStudio using the seqHMM library. I'm new to R and would usually do this kind of thing in Python, but I wanted to use seqHMM. I can see the model has been created and it ...
18 votes
5 answers
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Python library to implement Hidden Markov Models

What stable Python library can I use to implement Hidden Markov Models? I need it to be reasonably well documented, because I've never really used this model before. Alternatively, is there a more ...
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1 answer
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Reinforcement Learning control with known dynamic equation

I know there is model-based reinforcement learning. But all the approaches assume an MDP. If I want to do a feedback control of a system (i. e. control an inverted pendulum) it's quite easy to find ...
2 votes
1 answer
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Regime detection to identify transitions between habitats

The following figure represents the concentration of a substance (referred to as Element in the code) measured in an organism throughout its life. There are ...
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Ranking graph's nodes by score propagation

Problem I have the following directed tripartite graph $G(E\cup V\cup P, A)$, where there is a many-to-one symmetric relationship between the subsets V and E - $e\in E,v\in V,[e, v]\in A \iff [v, e]\...
17 votes
1 answer
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Visualization of multiple Markov models

I am working on a project where we compare over 10 different Markov models, each representing a different treatment plan. Most often single models are visualized with a decision tree or transition ...
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Estimating model for transition probabilities of a Markov Chain

Suppose that I have a Markov chain with $S$ states evolving over time. I have $S^2\times T$ values of the transition matrix, where $T$ is the number of time periods. I also have $K$ matrices $X$ of $T\...
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Machine Learning alternative for hashing

Is there a Machine Learning technique that can used to detect the slightest change in data? I know this can be done using a hash but I was just wondering if there is any machine learning technique out ...
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Best python library for training using Hidden Marov model with Gaussian Mixture

I would like to train my data using HMM- GMM (Baum Welch approach with gaussian Mixture) to find the best parameters suited for my data. Note : My data is ...
1 vote
1 answer
566 views

Q-learning when minimising a total cost instead of maximising a total reward

I have a decision problem where the results are measured as a cost that I want to minimise. It seems like a good fit to Q-learning, but I am not sure how to adjust it to deal with a cost instead of a ...
1 vote
1 answer
250 views

Predict how many days late or early someone will finish their work

So I have a set of deadlines and people, with a database of when those people finished their previous work and how much after the deadline it was, as well as when the work was given. The work itself ...
1 vote
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Reinforcement learning - generating a matrix of continuous values with varying size for test data generation

Currently, I am using RL A3C algorithm for test data generation, where for a set of 30 functions written in C (mostly basic algorithms like Prime number checks, triangle validity, etc.) I try to ...
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1 answer
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Evaluating value functions in RL

I'm working my way through the book Reinforcement Learning by Richar S. Sutton and Andrew G. Barto and I am stuck on the following question. The value of a state depends on the the values of the ...
1 vote
1 answer
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finding themes from text documents

I have a text documents that contain 1000s of abstracts from medical whitepapers. I want to find themes from that text. Any suggestions other than text clustering since clustering helped me to find ...
3 votes
1 answer
543 views

Reinforcement Learning - How are these state values in MRP calculated?

This is a question from the book an Introduction to RL, page 125, example 6.2. The example compares the prediction abilities of TD(0) and constant $ \alpha $ MC when applied to the below Markov ...
3 votes
1 answer
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What is the relationship between MDP and RL?

What is the relationship between Markov Decision Processes and Reinforcement Learning? Could we say RL and DP are two types of MDP?
3 votes
1 answer
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Should reinforcement learning always assume (PO)MDP?

I recently just started learning reinforcement learning and learned that reinforcement learning algorithms work under the assumption of MDP or POMDP. However as I read A3C and recent vision based deep ...
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2 answers
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Equations in "Intoduction to RL": What is the meaning and difference between E, and E with subscript?

This question is from An introduction to RL, page 78. In the formula below the page, both $\mathbb{E}$ and $\mathbb{E_\pi}$ are mentioned. Could you help me understand the difference between ...
1 vote
1 answer
203 views

MDP - RL, Multiple rewards for the same state possible?

This question is from An introduction to RL Pages 48 and 49. This question may also be related to below question, although I am not sure: Cannot see what the "notation abuse" is, mentioned ...
3 votes
1 answer
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What is the optimal value of a Markov Decision process with Single actions at each state?

I am trying to solve some questions about a MRP (i.e. a Markov Decision process with only one possible action at each state). The setup is as follows: There are two states ($a$ and $b$) stepping to $...
4 votes
3 answers
5k views

Markov Chains for sequential data

I am new to Markov chains and HMM and I am looking for help in developing a program (in python) that predicts the next state based on 20 previous states (lets say 20 states in last 20 months). I have ...